Artificial Intelligence is here to stay! Read this article to learn how to introduce children to AI so that they are prepared for the future.
This post was first published on eLearning Industry.
It has been a year since the release of ChatGPT, and educators are still scrambling to respond to this new kind of AI tool.
Much of the conversation has revolved around the double-edged nature of AI chatbots for educators. On the one hand teachers worry that students will suddenly cheat on homework with abandon, since chatbots can write essays in ways that are difficult to detect. On the other hand, though, educators see the potential of the tools to save them time on administrative tasks like writing lesson plans.
But in a recent working paper, a trio of education scholars say that these discussions are far too “parochial” and short-sighted. They argue that if the technologists building these new AI chatbots are right that the tools will quickly improve, then the technology will likely lead to massive shifts in knowledge work — including in academic research and the white-collar workforce — and therefore raise profound questions about the purpose of education.
“It just raises all these issues about what on earth are schools for?” says one of the paper’s authors, Dylan Wiliam, an emeritus professor of educational assessment at University College of London’s Institute of Education.
The paper imagines four possible scenarios for how generative AI, as the technology behind ChatGPT is called, might change society — and what those changes could mean for schools and colleges.
The goal behind the thought exercise is to get ahead of a rapidly changing technology, and to avoid what the scholars call the “worst-case scenarios” that could result. With that in mind, they close with a list of recommendations for how education and technology leaders can respond to try to best harness the benefits of the technology.
At times the paper is intentionally provocative. For instance, it imagines a scenario in which AI becomes so good at instantly creating learning tutorial videos and entertainment that people stop learning how to read.
“Literacy has been a relatively recent thing … and it’s actually really hard,” says Arran Hamilton, a director at the consulting firm Cognition Learning Group. “We have to co-opt a part of our brain that actually is generally used for facial recognition and we're borrowing that to use for literacy.”
After all, the scholars note, some research shows that the recent rise of GPS technology and mapping apps on smartphones have led people to become less able to read maps without the tools. Could it be possible that within a few short decades reading may, as the paper imagines, “become as quaint as Latin and the Classics—things that we learn for bragging rights and the conferment of social status, but not in the least essential (or even useful) for day-to-day living”?
For this week’s EdSurge Podcast, we connected with Wiliam and Hamilton to talk through what this AI-infused world might look like, and how educators can start preparing. They argue that the recent executive order by the Biden administration on the safe development of AI is a good start, but that it will take more big-picture thinking to respond to this technology.
Listen to the episode on Apple Podcasts, Overcast, Spotify, Stitcher or wherever you listen to podcasts, or use the player on this page.
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In the evolving landscape of education, one topic has taken center stage: generative AI. As educators, we tend to be on a continuous quest for innovative edtech tools that will enhance the learning experience for students. The potential of generative AI is both promising and profound, but it raises critical questions: How can this transformative technology be harnessed not only to educate but to empower inclusively and equitably?
Examining the Global Impact of Generative AI in Education and Beyond
In April 2023, Goodnotes initiated an exploration into the global utilization of generative AI within educational institutions, businesses and non-profit organizations. The objective was to discern how these innovative tools were being seamlessly integrated into daily routines and to ascertain their implications for the future of education. To achieve a comprehensive understanding, over 50 experts from 20 countries were convened, aiming to provide practical, implementable and secure recommendations for educational institutions.
The potential of generative AI is both promising and profound, but it raises critical questions: How can this transformative technology be harnessed not only to educate but to empower inclusively and equitably?
This research was motivated by Goodnotes' position as the AI-powered digital paper preferred by millions of students and teachers worldwide. When Steven Chan founded Goodnotes, he was a student at the time, and so his commitment to understanding the teaching and learning experiences of the community he built was essential to his mission, which is to revolutionize the way ideas are shaped and amplified. In addition, research contributors such as Dr. Alessandra Sala, the President for Women In AI and Director of AI and Data Science at Shutterstock, are clear about how much potential generative AI has in shaping education in the future.
The consequent published research is broken down into five recommendations that our contributors have highlighted as the most important, with diversity and equity at the forefront of our thinking. We understand that many schools around the world will be taking substantial steps to utilize this new technology, not just in handwritten digital note-taking but from bringing generative AI on board as a deputy head to recruiting directors of artificial intelligence. Indeed, premium private schools have a lot more scope to harness the potential of AI than the vast majority of public or state schools, many of which are underfunded and struggling to ensure that their students have equitable access to the subjects they want to study and their desired career paths.
For this reason, we intentionally collaborated with contributors from various backgrounds, including underprivileged schools, keen on exploring how generative AI could help educators better address student and community needs. Therefore, our recommendations prioritize facilitating discussions on the technology and providing practical deployment examples rather than prescribing specific solutions.
A Strategic Roadmap for Integrating Gen AI Into Schools
1. Create Guidelines: Create guidelines for the use of generative AI, ensuring accessibility across different regions and addressing diverse student and family needs. Involve various stakeholders, including boards of directors and caregivers. Prioritize safeguarding, child protection and adherence to regional regulations. Ensure that despite the excitement surrounding this technology, essential policies concerning child safety and data trust remain unwavering, as emphasized during the recent research publication event at DLD College in London Lord Jim Knight, Bukky Yusuf and Dr Andy Kemp.
2. Rethink Homework: Promote self-motivated study outside the classroom, encouraging students to deepen their knowledge at home. Discourage traditional homework assignments involving written tasks and essays, especially with unreliable AI detection tools. Remove grading for non-end-of-year assessment work. This recommendation stems from the experiences of students who have harnessed generative AI to enhance their understanding of subjects, promoting curiosity, confidence and deeper learning.
3. Transform Coursework and Assessment: Focus on redefining the process of completing coursework rather than altering the content, considering the various forms coursework can take. Encourage innovative classroom dynamics, such as co-working environments with AI 'copilots' to support independent student work. By transforming how coursework is completed, classrooms can become hubs of dynamic learning, empowering students to explore subjects more deeply with AI as their guide.
4. Promote Professional Development: Empower teachers to embrace AI-driven changes through closer collaboration with industry and the dissemination of AI's general capabilities and limitations. This recommendation centers on the importance of nurturing educators' confidence in adopting AI innovations through industry partnerships and knowledge-sharing within the teaching community.
5. Prioritize Equity and Diversity: Recognize the resource disparities between schools and the scarcity of technology in some regions. Encourage collaboration and knowledge exchange among schools and teaching communities, ensuring that no institution is left behind in the adoption of AI, with a focus on supporting teachers in pursuing their passions and addressing individual student needs. The emphasis on collaboration is particularly relevant in ensuring that even schools with limited resources can access the transformative benefits of AI in education, fostering inclusivity and diversity across the educational spectrum.
Inevitably, there will be schools that find this transition more difficult than others, and one of the consequences of this work is that Goodnotes has a strong network of determined and passionate schools and AI experts willing to help. We are eager to assist when it comes to ensuring that this transformation in education is as equitable as possible in providing access to knowledge and as diverse as possible when it comes to offering opportunities to learn and share experiences.
As the Director of Quantitative Research and Data Science, as well as the Data Privacy Officer at Digital Promise, I aim to demystify the complex world of data privacy, particularly in the realm of education and AI tools. Having begun my journey as an Institutional Review Board (IRB) committee member during my graduate school years, I've been committed to upholding ethical principles in data usage, such as those outlined in The Belmont Report. Collaborating with researchers to ensure their work aligns with these principles has been a rewarding part of my career. Over the past decade, I've grappled with the nuances of anonymous and de-identified data, a challenge shared by many in this field. In a time when student data is being captured and used more prolifically than we know, understanding how privacy is maintained is crucial to protecting our learners.
Anonymous Versus De-Identified
In a time when student data is being captured and used more prolifically than we know, understanding how privacy is maintained is crucial to protecting our learners.
The Department of Education defines de-identified data as information from which personally identifiable details have been sufficiently removed or obscured, making it impossible to re-identify a person. However, it may still contain a unique identifier that could potentially re-identify the data.
Similarly, the General Data Protection Regulation (GDPR) characterizes anonymous data as information that does not relate to any identified or identifiable individual or data that has been rendered anonymous to the extent that the data subject cannot be identified.
These definitions, while seemingly similar, often lack clarity and consistency in literature and research. A review of medical publications revealed that less than half of the papers discussing de-identification or anonymization provided clear definitions, and when definitions were provided, they frequently contradicted one another. De-identified data can be considered anonymized if enough potentially identifiable information is removed, as suggested in HIPAA data de-identification methods. Conversely, others contend that anonymous data is data from which identifiers were never collected, implying that de-identified data can never be truly anonymous.
Simplifying Data Privacy: Three Key Strategies for Educators
As AI tools become prolific in classrooms, it is easy to become overwhelmed with the nuance of these terms. Moreover, our news feeds are inundated with these conversations related to student privacy: Parents are concerned about data privacy, teachers reportedly don't know enough about student privacy and most school districts still lack data-privacy personnel.
In a time when the difference between anonymous and de-identified could matter greatly, what are educators to do about the data collected by AI tools they might use? I offer three overly simplified strategies.
1. Ask.
In 2020, Visual Capitalist developed a visualization of the length of the fine print for 14 popular apps and shared that the average American would need to set aside almost 250 hours to read all the digital contracts they accept while using online services.
If you do not want to spend hours researching whether the company collects and uses anonymous or de-identified data and how it defines it, you can always ask. A few examples of these questions include:
2. Give Students Choice.
The Belmont Report states that in order to uphold the Respect for Persons principle, individuals should be given the opportunity to choose what shall and shall not happen to them and, by extension, their data. Providing students the opportunity to choose whether they want to use an AI tool that will make use of their data whenever possible upholds this important ethics standard and gives students autonomy as they traverse this tech-rich world.
3. Allow Parents to Consent.
A further look at the Respect for Persons principle shows that individuals with diminished autonomy are entitled to protection. The Common Rule, or the federal regulations that outline processes for ethical research in the United States, states that children are persons who have not yet attained the legal age for consent and are one of the many groups entitled to this protection. In a practical application, this means that permission is needed by parents or guardians for participation, in addition to the child’s consent.
To the greatest extent possible, parents should also have the opportunity to understand and agree to a child’s data being gathered and used.
Let’s Navigate the Nuances Together
As someone who has been thinking about how to best protect students’ data since before you could wear your iPhone on your wrist, I regularly rely on these three strategies to best uphold the ethical principles that have guided my career. I ask when I do not understand, I strive to give individuals autonomy over their choices and their data and I seek consent when additional protection is needed. While these three practices won’t allay every fear one may have about the use of AI in classrooms, they will allow you to gather the information you need to make better choices for your students, and I have confidence that we can navigate the nuance together!
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As a lecturer at the Princeton School of Public and International Affairs, where I teach econometrics and research methods, I spend a lot of time thinking about the intersection between data, education and social justice — and how generative AI will reshape the experience of gathering, analyzing and using data for change.
My students are working toward a master’s degree in public affairs and many of them are interested in pursuing careers in international and domestic public policy. The graduate-level econometrics course I teach is required and it’s designed to foster analytical and critical thinking skills in causal research methods. Throughout the course, students are tasked with crafting four memos on designated policy issues. Typically, we examine publicly available datasets related to societal concerns, such as determining optimal criteria for loan forgiveness or evaluating the effectiveness of stop-and-frisk police policies.
To better understand how my students can use generative AI effectively and prepare to apply these tools in the data-related work they’ll encounter in their careers after graduate school, I knew I needed to try it myself. So I set up an experiment to do one of the assignments I asked of my students — and to complete it using generative AI.
My goal was twofold. I wanted to experience what it feels like to use the tools my students have access to. And, since I assume many of my students are now using AI for these assignments, I wanted to develop a more evidence-based stance on whether I should or shouldn’t change my grading practices.
I pride myself on assigning practical, yet intellectually challenging assignments, and to be honest, I didn’t have much faith that any AI tool could coherently conduct statistical analysis and make the connections necessary to provide pertinent policy recommendations based on its results.
For my experiment, I replicated an assignment from last semester that asked students to imagine how they would create a grant program for health providers to give perinatal (before and after childbirth) services to women to promote infant health and mitigate low birth weight. Students were given a publicly available dataset and were required to develop eligibility criteria by constructing a statistical model to predict low birth weight. They needed to substantiate their selections with references from existing literature, interpret the results, provide relevant policy recommendations and produce a positionality statement.
As for the tool, I decided to test out ChatGPT’s new Code Interpreter, a tool developed to allow users to upload data (in any format) and use conversational language to execute code. I provided the same guidelines I gave to my students to ChatGPT and uploaded the dataset into Code Interpreter.
First Code Interpreter broke down each task. Then it asked me whether I would like to proceed with the analysis after it chose variables (or criteria for the perinatal program) for the statistical model. (See the task analysis and variables below.)
Screen shot of Code Interpreter's task analysis. Courtesy of Wendy Castillo.Screen shot of Code Interpreter's variables. Courtesy of Wendy Castillo.After running the statistics, analyzing and interpreting the data, Code Interpreter created a memo with four policy recommendations. While the recommendations were solid, the tool did not provide any references to prior literature or direct connection to the results. It was also unable to create a positionality statement. That part hinged on students reflecting on their own background and experiences to consider any biases they might bring, which the tool could not do.
Screen shot of Code Interpreter's recommendations. Courtesy of Wendy Castillo.Another flaw was that each part of the assignment was presented in separate chunks, so I found myself repeatedly going back to the tool to ask for omitted elements or clarity on results. It quickly became obvious that it was easier to manually weave the disparate elements together myself.
Without any human touch, the memo would not have received a passing grade because it was too high-level and didn’t provide a literature review with proper citations. However, by stitching together all the pieces, the quality of work could have merited a solid B.
While Code Interpreter wasn’t capable of producing a passing grade independently, it's imperative to recognize the current capabilities of the tool. It adeptly performed statistical analysis using conversational language and it demonstrated the type of critical thinking skills I hope to see from my students by offering viable policy recommendations. As the field of generative AI continues to advance, it's merely a matter of time before these tools consistently deliver “A caliber” work.
Generative AI tools like the one I experimented with are available to my students, so I’m going to assume they’re using them for the assignments in my course. In light of this impending reality, it’s important for educators to adapt their teaching methods to incorporate the use of these tools into the learning process. Especially since it’s difficult if not impossible, given the current limitations of AI detectors, to distinguish AI- versus human-produced content. That’s why I’m committing to incorporating the exploration of generative AI tools into my courses, while maintaining my emphasis on critical thinking and problem-solving skills, which I believe will continue to be key to thriving in the workforce.
As an educator, I have a duty to remain informed about the latest developments in generative AI, not only to ensure learning is happening, but to stay on top of what tools exist, what benefits and limitations they have, and most importantly, how students might be using them.
As I consider how to weave these tools into my curriculum, two pathways have emerged. I can support students in using AI to generate initial content, teaching them to review and enhance it with human input. This can be especially beneficial when students encounter writer's block, but may inadvertently stifle creativity. Conversely, I can support students in creating their original work and leveraging AI to enhance it after.
While I’m more drawn to the second approach, I recognize that both necessitate students to develop essential skills in writing, critical thinking and computational thinking to effectively collaborate with computers, which are core to the future of education and the workforce.
As an educator, I have a duty to remain informed about the latest developments in generative AI, not only to ensure learning is happening, but to stay on top of what tools exist, what benefits and limitations they have, and most importantly, how students might be using them.
However, it's also important to acknowledge that the quality of work produced by students now requires higher expectations and potential adjustments to grading practices. The baseline is no longer zero, it is AI. And the upper limit of what humans can achieve with these new capabilities remains an unknown frontier.
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Since the release of ChatGPT nearly a year ago, teachers have debated whether to ban the tool (over fears that students will use it to cheat) or embrace it as a teaching aid (arguing that the tool could boost learning and will become key in the workplace).
But most students at K-12 schools are not old enough to use ChatGPT without permission from a parent or guardian, according to the tool’s own rules.
When OpenAI released a new FAQ for educators in September, one detail surprised some observers. It stated that kids under 13 are not allowed to sign up (which is pretty typical, in compliance with federal privacy laws for young children), but it also went on to state that “users between the ages of 13 and 18 must have parental or guardian permission to use the platform.”
That means most students in U.S. middle and high schools can’t even try ChatGPT without a parent sign-off, even if their schools or teachers want to embrace the technology.
In my eighteen years of working in education … I’ve never encountered a platform that requires such a bizarre consent letter.
“In my eighteen years of working in education … I’ve never encountered a platform that requires such a bizarre consent letter,” wrote Tony DePrato, chief information officer at St. Andrew's Episcopal School in Mississippi, in an essay earlier this year.
In a follow-up interview this week, DePrato noted that one likely reason for the unusual policy is that “the data in OpenAI cannot easily be filtered or monitored yet, so what choice do they have?” He added that many schools have policies requiring them to filter or monitor information seen by students to block foul language, age-restricted images and video or material that might violate copyright.
To Derek Newton, a journalist who writes a newsletter about academic integrity, the policy seems like an effort by OpenAI to dodge concerns that many students use ChatGPT to cheat on assignments.
“It seems like their only reference to academic integrity is buried under a parental consent clause,” he told EdSurge.
He points to a section of the OpenAI FAQ that notes: “We also understand that some students may have used these tools for assignments without disclosing their use of AI. In addition to potentially violating school honor codes, such cases may be against our terms of use.”
Newton argues that the document ends up giving little concrete guidance to educators who teach students who aren’t minors (like, say, most college students) how to combat the use of ChatGPT for cheating. That’s especially true since the document goes on to note that tools designed to detect whether an assignment has been written by a bot have been proven ineffective or, worse, prone to falsely accusing students who did write their own assignments. As the company’s own FAQ says: “Even if these tools could accurately identify AI-generated content (which they cannot yet), students can make small edits to evade detection.”
EdSurge reached out to OpenAI for comment. Niko Felix, a spokesperson for OpenAI, said in an email that “our audience is broader than just edtech, which is why we consider requiring parental consent for 13-17 year olds as a best practice.”
Felix pointed to resources the company created for educators to use the tool effectively, including a guide with sample prompts. He said officials were not available for an interview by press time.
ChatGPT does not check whether users between the ages of 13 and 17 have the obtained permission of their parents, Felix confirmed.
Not everyone thinks requiring parental consent for minors to use AI tools is a bad idea.
“I actually think it’s good advice until we have a better understanding of how this AI is actually going to be affecting our children,” says James Diamond, an assistant professor of education and faculty lead of the Digital Age Learning and Educational Technology program at Johns Hopkins University. “I’m a proponent of having younger students using the tool with someone in a position to guide them — either with a teacher or someone at home.”
Since the rise of ChatGPT, plenty of other tech giants have released similar AI chatbots of their own. And some of those tools don’t allow minors to use them at all.
Google’s Bard, for instance, is off limits to minors. “To use Bard, you must be 18 or over,” says its FAQ, adding that “You can’t access Bard with a Google Account managed by Family Link or with a Google Workspace for Education account designated as under the age of 18.”
Regardless of such stated rules, however, teenagers seem to be using the AI tools.
A recent survey by the financial research firm Piper Sandler found that 40 percent of teenagers reported using ChatGPT in the past six months — and plenty are likely doing so without asking any grown-up’s permission.
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In our increasingly digital world, educators recognize the significance of integrating AI tools in the classroom. AI integration can address diverse learning needs, promote data-driven decision-making, and spur class discussion. Leveraging AI in the classroom can enhance teaching while preparing students for a future where AI is integral to the workforce. It is essential for educators to tap into professional development (PD) opportunities to advance their understanding of how to use AI to improve the classroom experience.
ISTE U serves as a digital hub offering top-tier professional learning courses designed to assist educators in developing fundamental skills for teaching and learning in a digital world. Recently, EdSurge spoke with Chelsey McClelland, a third-year social studies teacher at Lawrence North High School in Indianapolis who recently completed the ISTE U course Artificial Intelligence Explorations for Educators.
EdSurge: Why did you decide to take the ISTE U AI course? What were your goals?
McClelland: One of the things that piqued my interest in the ISTE U course was that I didn't know much about using AI. I knew of ChatGPT from reading articles online, but I didn't know how it worked. I was just worried that my students would use this to write all their essays! And at first, our school blocked its use. I think all educators grew concerned.
I was at a crossroads; I didn't want students to use it because I didn't want academic dishonesty, but I also knew this technology would not go away. I thought it was really important to take the course because if I don't learn how to use it effectively and I don't learn what it is, then I won't be able to convey that to students. I'm doing them a disservice if I don’t learn about AI.
What elements of the course structure and content were particularly effective in facilitating your learning about AI?
The instructor was really supportive. The course seemed more focused on my outcomes — on my learning and applying the material — rather than meeting a deadline. I loved that the course was asynchronous and self-paced. As much as I love being a part of live PDs and collaborating with other educators across the country, it's really hard to do that weekly.
In the ISTE U course, we could regularly chat with classmates through posts and replies. I could still connect with educators but without having to be on a [video conference call], especially considering different time zones and start dates for school. My school started in early July, so I finished the course during the first few weeks of my school year. I appreciated having the flexibility to say, I'm super swamped this first week back at school and don’t have time in the evenings; I'm just going to sit down and do this on Sunday when I'm doing my lesson planning.
I think of it as levels of taxonomy: AI can give us a basic understanding, but then students need to analyze and create from there.
I really loved that every module opened with a fun way that AI can be used — it's not just ChatGPT writing an essay or MagicSchool AI making your lesson plan. One of my favorite activities was giving prompts to different generative art AI bots, resulting in completely different designs. Even the AI tools that are trained using the same information could still give unique results! This later became a great discussion point with my students.
Another great example from the course was a whole lesson on deepfakes, which involve creating audio or video of situations that never actually happened. Being a government teacher and teaching about political elections, I'm really excited to apply what I’ve learned in my classroom. This will help me educate students on spotting deepfakes and discussing the possible implications.
The crux of what I got from the course, which has helped me a lot in approaching AI and how to use it in the classroom, is that there are so many things to learn. As educators, we often think that it’s bad for students to use ChatGPT and that it's going to lead to them not learning anything. But we can't knock students for not knowing how to use it properly if we don't know how to use it properly ourselves; we need to teach them in what context to use AI and how to do it in an academically honest way.
AI can teach kids content, but it doesn’t teach them how to apply it. That’s our job as teachers.
Can you share specific examples of how you integrated AI concepts or tools into your teaching practices as a result of taking this course?
I work in a school with an emerging multilingual population, and AI has been so beneficial in helping me scaffold resources to make them more accessible for my students. Especially in a social studies classroom where I’m working with a lot of primary sources, sometimes it's hard to figure out how to break down that language so that English learners are still accessing the same content but not losing the academic vocabulary.
In government class the other day, we used AI to gather background information on past political parties. Once students understood the basic points, we then discussed how those parties may have merged into modern political parties.
I have also held class discussions about how AI works and how it is trained with information from the internet. This leads to conversations around what problems could arise. I ask students, “Could AI be trained on bad information? What can we do about that?” We discuss how we can’t blindly trust AI; rather, we can use it as a baseline to build knowledge. I think of it as levels of taxonomy: AI can give us a basic understanding, but then students need to analyze and create from there.
What advice can you offer other educators looking to learn more about integrating AI in their classrooms?
If we as educators are trying to prepare students for the world of college and careers, we must train them to use AI tools responsibly.
Don’t be afraid of AI. It seems like there exists this fear that AI will replace teachers. AI can teach kids content, but it doesn’t teach them how to apply it. That’s our job as teachers. Our role is changing a bit, but for the better. Now, we don’t necessarily need to spend as much time teaching the baseline information. Instead, we can do more projects, engage in more class discussions, and help students apply that information.
I encourage teachers to find their online communities. I follow several edtech accounts that offer ideas on using AI in the classroom, and I adjust the ideas to my different classes. This doesn’t mean redoing everything in your lessons. Try revising a couple of lessons for each unit by integrating AI tools. You don’t have to totally change what you are doing to expose students to AI.
AI is going to become a more integral part of the workforce. If we as educators are trying to prepare students for the world of college and careers, we must train them to use AI tools responsibly.
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Emerging technologies have the potential to reshape the educational landscape. From the earliest stages, as Pre-K parents search for activities and resources to nurture their child's growth, to K-12 schools adopting technology to improve student outcomes and operational efficiencies, the impact of modern learning tools is undeniable. The broader post-secondary landscape, including higher education and workforce development, has also quickly embraced online learning and up-skilling opportunities to better engage students and employees remotely.
Navigating this evolving landscape, edtech founders are confronted with many challenges in taking their products to market. Here’s how maintaining a focus on data, analytics and the responsible use of artificial intelligence can help edtech founders earn trust with their customers, unlock new opportunities and positively impact educational outcomes.
Key milestones in education innovation
Looking at the innovations in education over the last few decades shows how much the landscape has adapted and changed. In 2002, a critical transition occurred when 94 percent of public schools secured always-on broadband connections, granting educators and students increased access to rich media content. This was followed in 2006 with Amazon’s launch of Amazon Web Services (AWS), which enabled edtech entrepreneurs to build their solutions in the cloud, allowing them to rapidly and constantly iterate based on customers’ needs throughout the academic year.
By 2010, we saw a more intentional focus on learning standards, which provided curricular innovators an opportunity to develop materials tailored to the depth and rigor of the new standards, bypassing the constraints of outdated materials. Meanwhile, a trend that began in the 1990s was picking up steam — one-to-one computing. By 2015, technology had become an integral facet of learning, with devices ubiquitously present in students' hands.
Then, in March 2020, schools across the globe closed their doors, quickly pivoting to online instruction. Virtual classrooms became the norm. Edtech startups rapidly launched and scaled solutions, ranging from mental health supports to online proctoring tools. This abrupt transition not only changed the immediate education landscape but also left its mark on the future of education.
Today, as we navigate the post-pandemic era, the education industry is faced with numerous challenges, such as teacher shortages, declining enrollments and cybersecurity threats. Yet, this period is also marked by the promise of AI and the opportunity for educational institutions to develop robust partnerships with edtech startups to address these most pressing challenges.
Unique challenges of the education market
In education, alignment and collaboration are essential as edtech founders develop their go-to-market strategies. Founders must recognize that education sales involve multiple stakeholders, ensuring that varied perspectives are considered and the best solutions are adopted. They must align their planning to the academic calendar and the distinct buying cycles of the educational market.
Standards around security, compliance and compatibility are critical because they reinforce a commitment to safeguarding student information and ensuring quality solutions. And a recent push for evidence-based solutions with demonstrated efficacy increases the likelihood of positive implementations.
4 Ways to earn trust and unlock new opportunities with institutions
For edtech founders, the future is full of opportunities to reshape and elevate the learning experience. By maintaining a focus on data, analytics and the responsible use of AI, here are four ways founders can address some of the unique challenges in education to earn trust with customers and unlock new opportunities for growth.
The future of education is full of opportunities for edtech founders to transform raw data into actionable insights for educators. Combined with the undeniable potential of AI, edtech founders have an incredible opportunity to partner with educators and help solve some of the most pressing challenges in education.
Apply today for the AWS Education Accelerator
To help accelerate innovation in education for edtech companies, AWS has launched the new AWS Education Accelerator. The first cohort is focused on startups that can demonstrate early-stage traction with solutions aimed at enhancing the teaching and learning experience and improving educational outcomes.
The AWS Education Accelerator is a 10-week immersive program open to edtech startups globally. Applications are being actively accepted through November 17, 2023. This hybrid program will kick off with an in-person event at an Amazon office.
Recommended Links:
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We’re going to leverage AI wisely to spark a learning renaissance that, not incidentally, addresses teacher burnout and supports greater student success.
This post was first published on eLearning Industry.
AI is nearing significant development, but it requires data training to K-12 industry standards. Understanding student grades and demographics is crucial for AI to excel. Learn about the K-12 industry standards in data collection, and get one step ahead of your competitors.
This post was first published on eLearning Industry.
In this article, we explore ways that AI can be used to empower employees to take their productivity to new levels.
This post was first published on eLearning Industry.
Personalized learning is an educational approach that tailors the learning experience to the specific needs and preferences of each student. It recognizes and strives to accommodate differences in students' backgrounds, learning styles and abilities. As a result, each student deserves an education that is tailored to their individual needs and characteristics.
Technology plays a pivotal role in facilitating personalized learning, particularly through the application of data analysis and artificial intelligence (AI). Additionally, interoperability is a fundamental component in the realm of personalized learning, significantly enhancing its effectiveness and practicality. Armed with this holistic perspective, educators can make well-informed decisions about tailoring individualized instruction to effectively address the unique needs of their diverse student population.
Justin RoseRecently, EdSurge had the opportunity to speak with Justin Rose, the senior director of product management for data and analytics at Anthology, a provider of AI-enabled learning solutions. Rose shared his excitement about using technology to generate “novel, actionable and timely insights” to improve student learning experiences and operational efficiencies.
What does it mean to personalize learning? Why has it been a challenge for edtech companies to deliver effective solutions?
Rose: Personalized learning goes beyond tailoring the pace and the content of education to individual learners, though that is certainly part of the definition. Perhaps more importantly, it's also about creating an effective, ethical and equitable educational experience for every learner. That involves understanding the learner's cognitive style, their cultural background and even their emotional state or sentiment. It's a multidimensional approach that respects the learner's agency and the unique learning pathways that they may be on. And importantly, it also incorporates ethical considerations, ensuring that the technology used is transparent and data privacy is maintained.
I believe that personalized learning can democratize education, making high-quality learning experiences accessible to diverse populations. It can be even more impactful when it is supported by the kind of real-time, data-informed insights enabled by innovative technologies that institutional leaders can leverage for continuous improvement to the benefit of both learners and educators.
Personalized learning goes beyond tailoring the pace and the content of education to individual learners, though that is certainly part of the definition. Perhaps more importantly, it's also about creating an effective, ethical and equitable educational experience for every learner.
However, the challenges that are inherent to effectively implementing personalized learning, powered and extended by solutions that offer advanced analytics and AI, can be daunting. There are ethical considerations around data privacy, algorithmic transparency and equitable access that are paramount to going about this personalized-learning effort. There's also the challenge of ensuring that technology augments the human element in education rather than replacing it. So I think that involves and necessitates a significant shift in mindset for educators who have to learn to integrate technology into their teaching methods both effectively and ethically, but also a shift for administrators, policymakers, and other campus stakeholders who must reimagine conventional higher education technology ecosystems in their lived institutional contexts.
Another challenge that the sector is witnessing, perhaps more in the pedagogical dimension than the technological, involves the role of the educator, whether in-person, online, hybrid, high-flex or what have you, evolving from exclusively functioning as a lecturer to a facilitator or a coach. When this evolution matures, the result is a reshaped learning environment that operates as a dynamic and interactive space where students are actively engaged in their learning journeys as opposed to just having information shared with them. Shifting from teacher-centric to learner-centric education is a paradigmatic shift that we have known is necessary and that has been engaged along a number of fronts for some time now. However, the pandemic, a rapidly changing labor market, skills-based requirements for the occupations of the near and far future, and the evolving technological landscape have catalyzed and accelerated that shift of pedagogical focus from the teacher to the learner in recent years.
How does AI contribute to creating more personalized learning? How do data and analytics tangibly improve the classroom experience?
The perception of AI often simplifies it as one-size-fits-all, but in reality, AI is a diverse field with various algorithms and applications. In education technology, this diversity offers numerous opportunities to enhance personalized learning, from machine learning to predictive analytics, enriching educational experiences.
AI can act as a catalyst for educational innovation by providing insights into the most effective types of content and strategies, guiding continuous improvement. It's not only about making education more engaging; it's also about making it more effective. When students are engaged, they're more likely to retain information and apply it in a practical context, which is the ultimate goal of education.
A data-informed classroom provides another lens through which to view and evaluate student performance, complimenting educators’ own expertise and intuition. This allows educators to address issues before they become problems, allowing for more targeted and effective interventions.
However, it's important to note that data does not replace human judgment. Data can be a tool that can greatly enhance the education experience when used responsibly and ethically. Real-time analytics provides a level of granularity that was previously unattainable, enabling ongoing data-informed adjustments to the curriculum.
It's not just about improving academic performance, though that is an important component. It's about making education more equitable and ethical. By continuously monitoring the effectiveness of various educational strategies, instructors, advisors and other key stakeholders can identify and address issues of inequity and bias and ensure that all students have the opportunity to succeed.
The focus really remains on meaningful human interactions. Educators can use data and insights to guide student interactions, ensuring the technology enhances rather than overshadows the human elements of education.
Learners in a personalized education system are active participants in their educational journeys rather than passive recipients of information. AI should empower them to explore their unique strengths and challenges, set their own goals and monitor their own progress. This increases motivation and engagement by instilling a sense of ownership and agency. These are critically important factors in today’s educational environment. Students’ abilities in this environment, such as adaptability, critical thinking and self-directed learning, are exactly what they will need to navigate the complexities of the 21st century job market.
What is the significance of interoperability and integrated data models in the context of education?
It is really a matter of enabling meaningful, impactful decision-making at every level of the institution. Interoperability, integrated data models, advanced reporting and data exploration tools help to democratize insight and institutional intelligence across the organization. This means administrators, leaders and decision-makers are able to be more effective and move from the intuitive and anecdotal to the data-informed.
We know that the demands on and workloads of university faculty and advisors are significant and growing. Anthology offers a forthcoming advising tool that surfaces crucial learner engagement and performance data and helps educators make timely interventions. For example, one advisor shared about reaching out to a student whom they noticed in the progress tracker was having some difficulty in the course. The student later told that faculty member that if it weren't for that contact that the instructor made — if they hadn't reached out when they did — they wouldn't be enrolled anymore. They wouldn't be at the institution! The use of this technology by a human with the capacity to care and reach out made all the difference in helping the student to retain and persist at their institution and to continue their educational journey.
The focus really remains on meaningful human interactions. Educators can use data and insights to guide student interactions, ensuring the technology enhances rather than overshadows the human elements of education.
© Image Credit: metamorworks / Shutterstock
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This article is the second of a two-part series covering key principles to consider when integrating a generative AI creativity tool into your academic setting. Read the first article here.
The first two principles focused on how to ensure that the AI tool not only fits with existing technology and workflows but equips students for their futures. These next three principles provide guidance on what to consider in your AI tool evaluation.
Navigate the ethics of generative AI in education.
Generative AI is incredibly exciting but also opens the door to new questions about ethics and responsibility in the digital age. Thankfully, school systems have made meaningful headway in the past decade by integrating new media literacy and digital citizenship skills into the curricula, such that responsible AI can be a new addition to existing modules.
When selecting a generative AI tool, examine creators, training methods and ethical transparency. Not all image generators are equal; Adobe’s generative AI tools, powered by Adobe Firefly, were trained on Adobe Stock images, openly licensed content and public domain content where copyright has expired. Firefly is designed to be safe for commercial use, and thanks to guardrails encouraging appropriate use, it is ready to be used in classrooms. Plus, Adobe continually tests its model to mitigate against perpetuating harmful stereotypes.
Educators are just beginning to discover the most exciting ways that generative AI can engage students, drive deeper learning, save time and streamline mundane tasks.
In a world rife with misinformation, students also need authentication skills and responsible creation ethics. That's why Adobe has set the transparency standard through the Content Authenticity Initiative (CAI). Part of CAI’s work includes ensuring all Firefly output is properly labeled with secure metadata Content Credentials, which show the provenance, or origins, of digital file types, including how they might have been edited along their journey from creation to publishing. When it comes to education, this level of transparency and responsibility are paramount for everyone in a learning community, including students, educators, staff and caretakers.
Get started by exploring these content authenticity curricula crafted to help students better navigate the ever-changing digital information landscape with essential media and visual literacy skills.
Promote inclusion, feedback and bias reduction.
AI tools must be inclusive and reflect diverse perspectives. It is important to understand the data used to train classroom tools to ensure inclusivity and minimize bias. When selecting tools, prioritize those open to feedback for ongoing improvement, which indicates a good educational partnership.
At Adobe, we use varied datasets and refine our generative AI models to mitigate bias. Our generative AI features, like Firefly and Adobe Express, offer easy feedback options, enhancing dialogue. It's crucial that companies and organizations engage with educators, fostering collaboration to develop and improve generative AI for all.
By prioritizing ethics, inclusivity, feedback and community, we can empower students with the skills and knowledge they need to thrive in a digitally driven world, ultimately shaping a brighter future for education.
Get support with professional development, curricula and educator communities.
As with any new technology or transformative change, educators and school leaders will need a lot of support, inspiration and a like-minded community to bounce around ideas and share inspiration. When selecting a generative AI tool for the classroom, look for a holistic solution being offered beyond the technology, including ongoing professional development, curricula, community and other support.
Educators are just beginning to discover the most exciting ways that generative AI can engage students, drive deeper learning, save time and streamline mundane tasks. As we embark on this journey of learning, Adobe is collaborating with educators in K-12 and higher education to develop free opportunities for professional growth. This includes professional learning, in-person and virtual events, and Adobe Creative Educator (ACE) community groups all focused on collectively exploring the innovative realm of creative generative AI within the classroom.
After all, together is better. That’s why Adobe also leads conversations on AI in education with global and cross-industry education partners like the World Economic Forum’s Education 4.0 Alliance. Adobe has also joined the advisory committee for the TeachAI initiative, led by ISTE, Code.org, Khan Academy and ETS. In these ways, we invest in a broader, global coalition to learn from and help advise educators, leaders and policymakers with guidance, policies and a vision for continual improvement in student learning and outcomes.
© Image Credit: Generative AI in Adobe Express
Plato once quoted Socrates lamenting that, “If men learn this, it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written.”1 The ancient philosopher was speaking, of course, of the latest technology in the B.C. era: hand-written scrolls.
As humans, we’ve always had a somewhat complicated history with invention. On the one hand, we are driven to create tools that make our lives more efficient, but we also can’t help but feel a little uncertain of these new steps even as we are compelled to take them. With all new technology comes a great deal of trepidation that our previous ways of knowing are being lost. It goes with the territory, so to speak, whether we are living in ancient times or here in the 21st century.
With all new technology comes a great deal of trepidation that our previous ways of knowing are being lost.
Last spring, my university held an emergency faculty meeting about identifying papers written using artificial intelligence. Upon the release of ChatGPT in the fall of 2022, we noticed that some of our students had suddenly become “experts” at synthesizing research and organizing their term papers, often resulting in perfect scores on their written assignments. It was a bit of a dizzying experience trying to get ahead of the technology as we scrambled to find ways to cope with this impressive new platform, so seemingly well-equipped to help our students effortlessly complete their assignments.
We had been here before in the ’90s when we were all but certain that internet search engines had entirely ruined higher education. Despite those first days of insecurity, we decided to teach ourselves to lean in to technology rather than distance ourselves from it. Teachers and librarians began to integrate online formats into the learning experience, with great success and often with highly desired outcomes. It worked out perfectly!
But just as we were relaxing into a smug sense of mastery, AI happened, and it unexpectedly upended the whole deal.
With our lessons on leaning in not far behind us, we knew we had to embrace this new technology. We also knew that the detection software was not far behind, and in fact, it was released by the next term. The best way to harness a monster, after all, is to create a more powerful one.
Yet in our rush to control the technology, we may have initially overlooked the gifts that had been given to us via AI. In truth, this new invention can take us to next-level learning, and we are just now unlocking the full potential of this in our classrooms.
Ways that students and educators may benefit from using AI include:
Gender inequality in technology development and user design is a well-known challenge; expanding technology use is part of the solution. Melinda Gates has recently given a series of interviews sounding the alarm over the inherent male-centered bias of AI, among other concerns.
To shift AI toward being a more neutral, individual-oriented tool, educators can commit to teaching and supporting the use of gender decoders, which are algorithms that detect a lack of gender inclusivity, and other modalities aimed at achieving design balance.
On a grand scale, the United Nations is working on an agenda to address this and other concerns at the Global Digital Compact session in fall of 2024, which aims to “outline shared principles for an open, free and secure digital future for all.” At the individual level, though, we can each encourage and inspire the students in our classrooms to consider how to address such complex issues as gender bias in their own use (and design) of technology tools. The STEM gender gap remains an area of much-needed attention that we, as educators, can actively work on improving in our daily lessons.
AI offers people who are learning English new and improved avenues for more effective communication. Language AI modeling tools can assist learners with pronunciation, grammar and translation, responding receptively in real time through simulated chats and quizzing techniques.
Some formats even allow learners to generate 3D images based on their written instructions, creating an instant multi-sensory experience as they work on their language skills. The benefit to our classrooms is immeasurable here as it can engage students at their proficiency level, helping students keep up with their peers and the flow of class material.
This technology can be reversed too, assisting English-speaking students in foreign language acquisition. Such tools set the foundation for meeting the needs of a 21st century economy, where global communication skills are essential for students and teachers alike.
This is a game-changer for neurodivergent students or those with specialized educational needs. Visual or auditory challenges can effectively be addressed via AI, as programs can be coded to communicate via sign language or translate written words into speech. AI configurations can also provide students with audio, pictures, or project-driven materials according to personalized feedback.
One of the strengths of the software is that it can be prompted to explain the same concept in several different ways. This allows students the ability to repetitively work through difficult subjects until they locate an explanation of the material that resonates with them.
Additionally, researchers have found that some learners with autism or ADHD, for example, respond more positively to lessons provided by robots than other approaches that have been used. One of the key findings is that mechanized bots don’t demonstrate facial feedback that could be construed as unsupportive or judgmental. Broader research is currently underway, indicating that this is an area of growth investment for our schools.
I’m learning to accept the setbacks that might come with such giant leaps forward, hopeful that AI will provide us with innovative tools to help every student reach their highest potential.
Students shouldn’t use AI to write their papers, but it can help them get started with the hardest part: organization. In my classrooms, I encourage some students to use ChatGPT for expediency in brainstorming paper topics. This includes using AI for designing outlines, learning about core foundational designs (like how to build a thesis), or integrating appropriate stylized citations. In this way, it is the starting point for research, and the heavy lifting comes on the other end of the information garnered from the software.
For example, since AI is hugely prone to internal factual flaws, students must fact-check and cross-reference every statement generated by the tool. Critical thinking comes into play as students must locate, read and determine the legitimacy of each original source. The next step is to formulate and express their own ideas on the subject matter. In this way, we encourage a healthy dose of skepticism in our learners, motivating them to take control of tech-generated content rather than be passive consumers of it.
While the future of AI is unclear, I am taking a lean in approach as we journey toward this frontier. As with all new inventions (from hand-written scrolls to talking robots), I’m learning to accept the setbacks that might come with such giant leaps forward, hopeful that AI will provide us with innovative tools to help every student reach their highest potential.
1 Plato (1952) [c. 360 B.C.E.]. Phaedrus. Translated by Reginald Hackfort. pp. 274c-275 b.
© Moor Studio / Shutterstock
This article is the first of a two-part series covering key principles to consider when integrating a generative AI creativity tool into your academic setting. Read the second article here.
The 21st-century classroom is a dynamic, ever-evolving space where cutting-edge technologies, like artificial intelligence (AI), are pushing us all to rethink what students need to learn and how that learning can best be structured to prepare them for the future. One of the most exciting advancements is the emergence of generative AI, a technology that can help produce entirely new creations with just a few directives.
From creating realistic images and art to producing music and writing, generative AI's potential to help students learn and demonstrate their creative thinking skills in fundamentally new ways is truly awe-inspiring. What’s more, at a time when student engagement is more important than ever, creative AI tools can serve as powerful platforms for active, student-directed learning, motivating young learners to showcase their individuality and unique perspectives.
Of course, like with all new technology in education, the promise is coupled with uncertainty. Will generative AI tools like ChatGPT enable plagiarism or complicate assessment? How can schools stay fully informed about how generative AI models are built and trained? What safety and anti-bias measures are in place, especially for younger students?
Perhaps the biggest question on the minds of educators, caretakers and policymakers is this: How can we best integrate these new capabilities into our education system to set up students for success in the classroom and beyond?
The following principles can help ensure your education community is learning how to use generative AI responsibly with user-friendly tools made to enhance creativity and prepare students for the future.
Understand the value of creativity.
Creativity is often connected to the arts, but it’s an essential skill to all disciplines and fields because it teaches students how to solve problems in new ways, communicate ideas effectively, collaborate with others and find their voice through exploration and curiosity. ISTE recently outlined why it is more important than ever to teach creativity — how it motivates students to learn, lights up higher-order cognitive skills, sparks emotional development and inspires those who are hard to reach.
In the rapidly evolving job market, knowing how to use AI to fuel creative thinking and express ideas in powerful, creative ways will be essential for almost every job and career.
Even though 65 percent of students learn more effectively by doing and creating, the opportunities to do so are too rarely available. Imagine if every student learned how to apply creative thinking to every subject throughout their educational experience, from K-12 through college. By intentionally nurturing and scaffolding creativity and creative problem-solving throughout the curricula, educators can empower students with the critical skills they need to confidently navigate their lives and effectively apply them to emerging technologies, like AI.
Beyond academics, creativity is also essential for career success. According to the World Economic Forum, creative thinking is the second most in-demand skill organizations will prioritize in workforce development initiatives through 2027, followed by AI. In the rapidly evolving job market, knowing how to use AI to fuel creative thinking and express ideas in powerful, creative ways will be essential for almost every job and career.
This collective research tells us we need to transform teaching and learning experiences to help students master creative thinking and generative AI skills. And if students learn them with trusted, industry-standard tools, they will be readily equipped for the future.
Supercharge creative thinking, don’t replace it.
Calculators in the classroom finally shifted from controversial to commonplace when we saw how they enhance math skills without replacing them. Behind that success was a thoughtful, strategic implementation plan to use calculators at the right time for the right tasks, ensuring students become hyper-aware of the thinking processes calculators support.
Likewise, classroom generative AI tools should fit smoothly into current teaching methods, technology and creative skill-building. These tools enhance the creative learning journey across subjects, from idea formation to improvement and presentation. Encourage metacognition by prompting students to reflect on how generative AI tools shape their thinking and processes in various ways.
Classroom generative AI tools should fit smoothly into current teaching methods, technology and creative skill-building.
Adobe Express for Education is designed to integrate with existing technology and learning workflows, including rostering tools, learning management systems, safety and privacy features and the ability for K-12 administrators to toggle generative AI features on and off.
As we embark on the journey of integrating generative AI tools into our classrooms, it is clear that we are on the precipice of a transformative era in education. There is immense potential for generative AI to foster creativity, engagement and critical skills among students. The questions and uncertainties surrounding the responsible use of generative AI are valid, but they should not deter us from harnessing this powerful technology. Instead, they should motivate us to adopt a thoughtful and strategic approach.
As we move forward, it is crucial that school leaders, educators, caregivers and policy-makers fully recognize the value of creativity as an essential skill. Creativity transcends subject boundaries and prepares students for the future job market, where creative thinking and expression are highly prized. The integration of generative AI, when done with trusted industry-standard tools, equips students with the essential skills they need to thrive in an ever-evolving world. Together, we can embrace this technology's potential to transform classrooms into hubs of creativity, innovation and preparation for the challenges of the future.
© Image Credit: Adobe Stock
Julie York, a computer science and media teacher at South Portland High School in Maine, was scouring the internet for discussion tools for her class when she found TeachFX. An AI tool that takes recorded audio from a classroom and turns it into data about who talked and for how long, it seemed like a cool way for York to discuss issues of data privacy, consent and bias with her students. But York soon realized that TeachFX was meant for much more.
York found that TeachFX listened to her very carefully, and generated a detailed feedback report on her specific teaching style. York was hooked, in part because she says her school administration simply doesn’t have the time to observe teachers while tending to several other pressing concerns.
“I rarely ever get feedback on my teaching style. This was giving me 100 percent quantifiable data on how many questions I asked and how often I asked them in a 90-minute class,” York says. “It’s not a rubric. It’s a reflection.”
TeachFX is easy to use, York says. It’s as simple as switching on a recording device.
“With other classroom tools, I have to collect the data myself. And the data usually boils down to student grades,” York explains. But TeachFX, she adds, is focused not on her students’ achievements, but instead on her performance as a teacher.
Generative AI has stormed into education. Most of its applications, though, are either geared toward students (better tutoring solutions, for instance), or aimed at making quick, on-the-spot lesson plans for teachers.
Bubbling right under the surface is a key question: Can AI help teachers teach better?
“Teaching is hard. Helping teachers be the best version of themselves takes a huge investment of time and energy, and schools just don't have the resources. So most teachers don’t get the support they deserve,” says Jamie Poskin, the teacher-turned-founder of TeachFX.
Poskin says most teachers know good teaching practices, but need a little revision (or reflection) from time to time. These practices are largely based on giving students more voice in the classroom, so the balance of “talk” between a teacher and their students isn’t heavily skewed toward the former. For instance, teachers may consider replacing one-sided lectures with more group discussion, or they may make sure to ask follow-up questions to students’ answers.
“For student outcomes to change, something has to change about what the teacher is doing in the classroom. That behavior change is very hard,” Poskin says.
For student outcomes to change, something has to change about what the teacher is doing in the classroom. That behavior change is very hard.
Poskin cites anecdotal evidence about teachers who, after using TeachFX, realized they were inadvertently calling on some students to discuss answers more than others. These students often tended to be white and fluent in English.
Poskin, who started TeachFX while still a graduate student, says he wanted to figure out how to help teachers improve their instruction in a scalable way. “When teachers make two recordings, we can already see them asking more open-ended questions in the second one. We’ve been able to create an inexpensive observer effect,” Poskin claims.
These observations generated by AI can take quick effect. Keara Phipps, an elementary school teacher from Atlanta, says that TeachFX showed her she “talked too much” in her classes. With that feedback, Phipps brought down the ratio of teacher-to-student talk to 50:50. “Students should be equal participants in their learning,” says Phipps.
Many teachers might be surprised to realize just how much they speak compared to their students.
“We did a study of 100,000 hours of audio of non-TeachFX users. You want to guess how much the average student spoke in one hour of class?” Poskin says. “Seven seconds, per hour.”
TeachFX is the visible front-end of a collective effort that’s using AI to scale effective, quick and completely personalized feedback to teachers. At the Institute of Cognitive Science at the University of Colorado Boulder, Jennifer Jacobs has put raw classroom audio through automated speech recognizers and then natural language processing to generate feedback that tells teachers how many times they followed a “good” classroom practice, like asking their students to give the evidence behind an answer. Her application is called TalkMoves, and a version of Jacob’s research is now being used by the tutoring company Saga Education to train first-time tutors.
This kind of personalized feedback, made possible by AI, isn’t place- or time-bound, and that’s what makes it scalable, says Yasemin Copur-Gencturk. A researcher at the University of Southern California, she has been working on AI-based professional development for math teachers for several years.
Initially, she claims, there was pushback. “Many did not see the need for this kind of PD,” she says.
Copur-Gencturk persisted, supported in part by a federal grant, to create a tutoring-style platform for teachers, as yet unnamed. It features a talking digital avatar that helps teachers unpack common misconceptions that their students carry in mathematics. “If teachers know how students are going to respond to a learning activity, they can tailor their instruction,” says Copur-Gencturk.
AI-based professional development is gaining traction at a time when a record number of teachers are feeling burned out, underpaid and demoralized about their profession. The makers of these AI tools believe that technology can help stem the tide out of the profession. While tools can’t necessarily replace human coaches or in-depth professional development that districts conduct, they can help teachers take stock, and correct course.
Copur-Gencturk says the frequency and quality of the feedback shouldn’t depend on how rich or poor a school district is. All teachers should have equal access to tools that can improve their teaching. Yet for that to happen, these fledgling tech solutions need to find a way to pay for themselves, or convince early adopters to shell out.
“I wanted to get TeachFX for my entire school. But even for a small cohort of 10 teachers, they were going to charge the school $5,000 per year,” York says — the average cost for a pilot package. That’s much more than a department’s annual budget in her school, says York.
AI tools will also have to have to reckon with teacher concerns about where all that data about their instruction ends up.
Providing teachers with one-on-one, personal feedback is an ambitious goal. But it’s humanly impossible to bring that level of attention to every teacher’s class. It’s time- and cost-intensive, and potentially intrusive to teachers who don’t want to feel judged for their teaching styles.
“This is why the computational power we have now is exciting. Large language models can analyze classroom discussions at scale. To get more evidence out of a classroom is a precursor to explain everything else, like [understanding] student outcomes,” says Dora Demszky. Demszky is an assistant professor in education data science at the Graduate School of Education at Stanford University, and she’s part of an expanding group of academics feeding classroom audio to large language models to generate automated feedback for teachers.
Large language models can analyze classroom discussions at scale. To get more evidence out of a classroom is a precursor to explain everything else.
The audio-to-AI tool works like this: Recordings from a classroom, which include both teacher and student voices, are fed to a large language model. This has been trained, generally, on what “good” teaching practices sound like. For instance, if a teacher asks follow-up questions, or asks students to argue their point, the model is going to pick it up, identify it as an action, and show the teacher how many times they did that action in class. Both Poskin and Demszky say that the data itself doesn’t qualify their instruction style as a good or bad one, but rather offers a neutral report.
In May, Demszky and her colleague released findings from a study they conducted on more than 1,100 tutors who were teaching a free introductory coding course to about 12,000 students online. The tool they developed, M-Powering Teachers, led the tutors to reduce their own talk time by 5 percent in mentoring conversations, and their “uptake of student contributions” was up by 13 percent. “Uptake” here refers to a teacher revoicing a student’s contribution, elaborating on it or asking a follow-up question — teaching practices that give students more agency. These increased numbers, Demszky claims, offer good evidence that teachers can quickly respond to, and incorporate, objective feedback.
Evolving AI technology has made this feedback sharper. Poskin says the TeachFX application can pick out the richest teaching moments — like asking students follow-up questions, and affirming student responses — from classroom audio, and then show teachers how many times they employed these strategies. This feature wasn’t possible to add six months ago.
Jacobs, the researcher from the University of Colorado Boulder, conducted her own study in 2019 for an application that her team developed called TalkMoves. Jacobs has been working on a version of TalkMoves since 2017, thanks to a couple of grants she received from the National Science Foundation. Jacobs gave educators cameras to record videos in their classrooms, and then automated speech recognizers extracted audio, fed it to the natural language processing models and logged the teachers’ speech according to certain “discourse” markers that the model had been trained on. The TalkMoves application was one of the first apps of its kind to include a teacher interface that displays feedback in an accessible manner, claims Jacobs.
When COVID-19 hit during the study, in-person recordings had to stop, but Jacobs says some teachers continued to record their online classes. In the second year, when some of the instruction became hybrid, teachers recorded both online and offline instruction. The dataset shrunk from 21 to 12 teachers between the two years, but Jacobs observed an increase in teacher activities, or “moves,” like getting students to relate to each others’ answers — an improvement that researchers attribute to teachers using feedback from TalkMoves. Interestingly, says Jacobs, there wasn’t a significant difference between online and offline recordings when it came to the uptake of “good” talk moves by teachers.
Mandi Macias has personal experience with this kind of evolution. She’s taught fifth grade for 25 years in the Aurora Public School system in Colorado. After teachers there asked for better professional development tools, the principal at Macias’ old school introduced TeachFX. Macias used TeachFX every week last year and claims that she has since changed her whole teaching style from “lecturing” to “asking questions.”
“Students are also doing the heavy lifting with me in class. I’m not satisfied when they just agree or disagree with each other. They can now bring the best evidence for their answers,” Macias says.
Being able to listen to her class recordings — coupled with the TeachFX data dashboard — meant Macias could create a new model of conversational learning for her class. Currently Macias says she doesn’t have access to TeachFX since she switched schools.
Not all teachers may need or have time to sift through the transcripts generated by TeachFX and similar tools. York, the teacher from South Portland High School and Macias, the teacher from Aurora Public School system, both agree that teachers have to put in the work to change, once they see the data.
“I’ve been in PD sessions where teachers fall asleep or walk out. Teachers often make the worst students,” says York.
But what’s undeniable about TeachFX’s feedback and Copur-Gencturk’s digital mentorship platform is that all this data is personal. This is why the one-on-one sessions work, says Copur-Gencturk.
Her solution involves a low-voiced AI mentor that pops up on one side of the screen (like a colleague in a Zoom call), and walks teachers through different problem sets. This kind of professional development looks most like what students might go through with an AI assistant. Teachers can either type or voice their responses.
Copur-Gencturk spent two years building the dataset that would eventually train the AI tutor. For this, she had to log every conceivable problem that students might encounter in a math lesson. For instance, students could have challenges moving from simple addition to the multiplicative reasoning that’s needed to study ratios. “Teachers need to know how students are approaching a math problem and what their responses indicate about their understanding. The program helps teachers ask the right questions to find out,” says Copur-Gencturk. The mentoring is punctuated with actual classroom videos that show teachers how these problems are solved.
The system has checks and balances, because the AI doesn’t let teachers move on to the next activity until their response meets the learning goals of the set activity, says Copur-Gencturk. This could feel limiting, except teachers have the option to pause and come back another time. This isn’t possible with in-person professional development.
A screenshot of Copur-Gencturk’s AI-tutoring platform.Copur-Gencturk wants this AI program to become a part of pre-service teacher training, especially for math. What would be even better is to link student diagnostic tools with the kind of professional development she’s building. That way, says Copur-Gencturk, teachers will know what misconceptions to attack.
Both TeachFX and the virtual assistant have common goals: make professional development personalized, safe and easily scalable. If it’s priced competitively — the AI mentor isn’t a commercial product right now — then personal professional development can also be accessible to every teacher.
Teachers, the target of all these innovations, have to be on board. York says she loved working with TeachFX, but when she sent it out to a group of 80 fellow teachers in her district, she got zero sign-ups. “There’s no judgment here. They may not have had the time. But some CS [computer science] teachers just didn’t want to know feedback about their instruction,” says York.
Teachers don’t always want to be recorded because, York claims, the data could become punitive in districts’ hands. Poskin, of TeachFX, asserts that the data the tool collects is only intended for the teachers’ personal use, unless they choose to share it with a mentor or observer.
The issue of data sharing is a sensitive one, says Demszky of Stanford, and rightfully so. Making sure that the classroom data is only shared with the right people is the first step.
Demszky admits there has been a mixed reception from school districts — some are more open to tech innovation than others. “Teachers are already using tons and tons of tools where their data is being shared. It’s happening in many contexts. This is a new context we are trying to share data in,” says Demszky.
Phipps, the teacher from Atlanta, says teachers may find it difficult to take constructive criticism from an app’s feedback. “This isn’t subjective. It’s taking a deeper look at your work. You’re going to have to change something when you look at this data,” Phipps says.
New personalized professional development tools will need their own champions and early adopters. Phipps says she’s open to observers looking at her classroom data, and she already has suggestions for TeachFX: a crossover app with Swivl, a classroom management tool that records teachers as they move around a classroom.
“Then I can see and hear what’s going on. It could spark new seating ideas, for example,” Phipps says.
York says she already had an open-door policy about her teaching style. She teaches a diverse set of students, some of whom are learning English, and she wonders whether TeachFX can evolve to better support them.
“It would be interesting if the app picked up the many languages spoken in class. Or if it picked up students translating for each other,” York says. “How many times is more than one person speaking? How many times are groups talking?”
But York is willing to give it more time before expecting these tools to become perfect.
After all, she says, “We didn’t expect Siri to pick up all our idiosyncrasies from day one.”
© Master1305 / Shutterstock
Leveraging AI-powered learning experiences can help create an engaging environment for learners. AI-based tools can provide personalized learning paths, tailored to the individual’s needs and goals. They can analyze an action to ensure that it is aligned with their goals.
This post was first published on eLearning Industry.
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