We contribute to current and emerging public conversations and policy development related to edtech and AI in education. We also curate resources that we find helpful in navigating the rapidly growing domain of AI-enabled edtech.
Please note that inclusion in our curated list does not constitute endorsement of all the views in a resource.
If you would like to suggest a resource for potential inclusion, please contact us here.
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Report: Artificial intelligence, cognitive offloading and implications for education
This report investigates a profound new challenge that we face with AI’s power to rapidly access information and provide a semblance of thinking: the risk that students will outsource too much of the cognitive work that is crucial to establishing the knowledge, skill and ‘thinking infrastructure’ that enables both schooling success and lifelong capacity for ongoing learning and understanding.
There is a growing body of evidence that using AI can short-circuit the cognitive effort required for sustainable, deep learning, thus creating ‘false mastery’ with potentially long-term consequences. This cognitive offloading from human to AI is especially risky for school students (‘novice’ learners who are building foundational knowledge and skills) when they turn to AI as a tempting substitute, not an amplifier, increase their dependency on the tool and lose access to deeper learning. It also introduces extra equity risks for disadvantaged students.
Importantly, the report also outlines how these harmful effects can be counteracted through specific purposeful teaching and learning strategies and effective design of AI education technology, anchored on bolstering the central role of teachers.Report: Securing digital equity in Australian education
Clear steps are required to harness the opportunities that educational technology and AI offer in Australian classrooms and prevent a growing digital divide that is further entrenching inequality for students experiencing disadvantage.
This report calls for policymakers to establish a Digital Equity Learning Guarantee to address data and design inequities in digital tools and ensure all Australian students can access and benefit from the highest quality digitally enabled teaching and learning resources (‘edtech’).
Article: The new wave of change: Artificial intelligence and education
This article offers 5 key questions for educators to consider when choosing and integrating educational technology (edtech) into their teaching practices. It highlights the importance of prioritising pedagogy over technology, and ensuring that AI and edtech tools are used to enhance teaching while maintaining ethical standards, inclusivity, and data privacy.
Video: Can technology facilitate scale? In conversation with Dr Monica Bhatt
In this recorded interview we spoke with Dr Monica Bhatt about the importance of leveraging edtech in the pursuit of educational equity. She also stepped us through her research, which looks at the benefits of blended intensive learning support i.e., combining small, in-person group tutoring with AI-enabled learning applications. This discussion was facilitated by Dr Kelly Stephens, Director of Edtech and Education Policy at the UTS Centre for Social Justice and Inclusion.
Read transcript
Transcript
So thanks, Monica, very much for joining me today. I'm going to begin this conversation by acknowledging that I'm joining you from Aboriginal land, from the land of the Gadigal people of the ER nation, and pay my respects to elders past and present and my respects to all First Nations people. So we're here to talk about your study which explored scaling intensive tuition through the addition of a computer based learning application. And my first question to you is what prompted you to undertake this study?
Sure. Well, first, thank you so much for having me. I'm, I'm, I wish I could be there in person, but alas, the world is large. And I am thrilled that technology is facilitating my ability to be there in the room with you today. And it, it really is at the heart of this because technology can facilitate the so many things in our society, including the advancement of teaching and learning so that all students benefit. And yet there are some guardrails that I think we we want to place along that
we wanted to explore that as part of the study. So for the past decade or so at the University of Chicago Education Lab, we have been looking at the effects of what we call high dosage tutoring. So this is a tutorial where one tutor works with a few students in the context of the school day as part and parcel of how instruction is delivered during the school day.
And we wanted to understand the impacts of that intervention on student learning. So about 10 years ago now, we did a series of randomised control trials, which is the most rigorous type of study that you can do to understand the causal impact of one sort of intervention or programme on outcomes. And we saw that when students meet with a tutor in ratios of two students to one tutor, it is enormously beneficial to them. And in this case, it was a study of 9th grade algebra, which seems to be a gatekeeping course, at least here in the United States and I imagine in Australia as well, to later life outcomes. We saw that it was so beneficial and very cost effective. So we went running for the hills and told all of our public sector partners in Chicago, across the country, at the Departments of education, at various States and districts. And we said, you know, we found the educational equivalent of nuclear fission. This is how we're actually going to address these longstanding disparities in achievement that we see along race and class lines that have been very persistent in the US educational context. And they said that's wonderful. It is cost prohibitive because it costs $3000 per student, which is about 1/4 or 25% of the per pupil expenditure of Chicago Public Schools annually.
That's too high to spend on one student and one grade in one subject. And so they sort of tasked us with trying to find an intervention that was just as effective, but cheaper and less costly. And we said to our partners at the time, Saga Education, who was the programme delivery partner, there's sort of a best in class nonprofit tutoring provider. We said, how can, how can we do this? And they went off and did some design work, and they came up with an interventional design that was for students sitting at a table with one tutor, you know, similar sort of structures. It was an elective course called Math Lab. The kids went every day. It was part of their schedule. So none of that changed. But you, for the same amount of money for the same tutor, could serve twice as many students. And in order to facilitate that larger group, the students spent every other day on an educational technology platform. In this case, it was Alex. But you could sub in any kind of adaptive educational technology platform that helped you differentiate instruction. And we said, does this work? Now oftentimes in social science, when you reduce the cost of something, you reduce the efficacy as well. And so we wanted to understand, could you cut the cost in this case? It cut the cost of this very effective tutoring intervention that doubled or even tripled what a student learned in mathematics in one year compared to their peers down the hall in the same school in the same grade. We said, can you cut the cost of that by 30 to 50%, let's say 30% just to be really conservative here and maintain the effectiveness. And what we found was that it was just as effective. So I know we'll get into the the study findings in a minute, but we were really motivated by whether you could sort of use this design recipe of increasing the ratio of students to tutors just slightly and then leaning on the educational technology to facilitate that scale and still mimic the differentiated instruction. We wanted to know, could that interventional design work?
I thank you. I love that phrase. Leaning on the educational technology, helping it to leverage for equity, which you have described as being at the very heart of of this study. Now is it fair to say that boiled right down what your study found was that by introducing the technology platform you could effectively double the number of students that the programme reached, keep the same the number of human tutors, which is a really big consideration in a kind of resource constrained environment. Reduce as you've just said, the per cost student significantly and yet not compromise the significant learning outcomes that you've seen in the previous human only study. Is, is that correct?
That's right. So we saw increases of .23 standard deviation. So I apologise for using a term like that. But we saw basically no difference in the amount of learning for a 9th grade student in algebra, whether they had the sort of SAGA technology enabled version or the prior version where it was two students working with one tutor. And so again, we were really thrilled by this because it provided a road map or a recipe, if you will, of how we could maybe think about taking this very effective intervention delivered by a boutique vendor at modest scale, maybe a few thousand students and really think about how we could reach millions of students, you know, across the country and even across the the world. And you said you were thrilled, which I can absolutely understand. Were you surprised?
Um, you know, I think something that people don't appreciate as much is that there was a lot of design work and iteration that went into the, the intervention itself done by Saga Education prior to us studying its efficacy. And so personally, Saga had sort of given their stamp of approval and said, OK, this is the version to test. We had done other iterations that ended up on the cutting room floor. So we had tried 1/2 dose version where kids met every other day.
But in the context of a school building that's very confusing for kids because they have a new schedule every other day and principals didn't like that very much, neither did the teachers. But in this version it was on the student schedules and it was expected by teachers, tutors and students alike what what was going to happen. And so they liked this version better than the sort of alternate day. So we tried lots of different iterations. And so I don't know that I was surprised that it was effective. I am surprised that it was just as effective that there was no deterioration in treatment effects just because that's something we we never see in social science. And it's, it's really heartening because it means that technology can facilitate scale. And now we have to get even more sophisticated in our research questions and our practise and understand under what conditions. So that segues very nicely. The next thing I'd like you to talk about is, in your view, what were the primary factors that supported the success of this implementation?
Sure. So oftentimes when we think about introducing technology, there are no standards attached to that phrase. So we could mean everything from enabling kids to have a login from the administrative side, like, oh, everybody is able to get a login and a passcode, all the way to what this saga technology intervention was, which is it is a course that had to be scheduled into a student school day. That means time was found, dedicated time for the student to go to a classroom where there was a teacher of record and six tutors. So 24 kids were scheduled into this class period. They met, you know, with a tutor.
The tutors had to be sourced, staffed, hired, managed, trained, and the kids sat at the table with with the tutor. So there was still 4 kids sitting at a table with one tutor. And so even when they're on the Ed tech platform, they would start off by the tutor checking in with the kids both interpersonally. And they had this thing called the do now where they would all work on a math problem is kids filtered in. They would check in with the students about halfway through the class period when the the two students that they were working with we're working on a math problem, They would lean over and say, hey, how's it going over there? Are you, you know, making sure they're not texting their girlfriend or, you know, doodling or, or just staring at the computer screen and not actually engaging. And by having that sort of motivational piece that the human tutor could do, but allowing the platform to do the differentiated instruction and get kids to practise math problems, which is miraculously what gets kids to learn more math is, is that practise? I think that interventional design and those kind of component pieces were really important because the kid wasn't just, you know, on a zoom with a black screen. They were, they had a, a human tutor who expected them, who was motivating them and they had access to this technological platform. I think that's just a very different interventional construction. Then I'm going to give you a slip of paper with a login and you can log in on your own time. I worry a little bit about that use of technology in that construction because we know that the kids who are more likely to log on are the kids who are already doing better in school, or at least we have mounting evidence that that's true. Would you just that that's effect is described in your paper, we sometimes call it the Matthew effect of, you know, increasing advantage, building on advantage. Would you describe a little bit more about what you did find about those different patterns of edtech usage?
Sure. So I should be really clear here that there is a lot more work to be done on this topic. So I'll try to stick to what this study showed and then try to differentiate that from my opinion. So in this study, we had the technological usage of the kids who logged on to the Alex platform. So by definition, these were all the kids who in our random assignment trial were assigned to receive tutoring. And the tutoring was inextricably linked to logging on to the Alex platform. And even in that constrained design setting, when we looked at the relationship between students baseline characteristics and Alex usage, we found that kids who had higher test scores and higher GPA's were more likely to log on to Alex and use it more, learn more topics than students who didn't have those characteristics. Now, is that causal evidence? Absolutely not, because this is just descriptive in that sort of one, one use case from our study, but I think that is the pattern that is replicated in other studies as well. And we're starting to learn that, you know, left in the wild. And now I'll sort of veer into my hypothesis. We see that kids who sort of log on and do their work either already have the study skills, the motivation, the attachment to schooling and also the inclination to do that. And so they're the kids who kind of had higher baseline test scores and GPA's to begin with. And I think we have mounting evidence of that from the COVID-19 pandemic in which we have a lot of data coming from remote instruction and you can see who logged on and who dropped off. And and so I, I would sort of count that evidence from the remote instruction during the pandemic as as substantiating this hypothesis.
Thanks very much for that, Monica. And I think that moves us kind of naturally to the next question that I'd like to ask you, which is were there any aspects of this particular study that you feel warrant now further exploration? You already mentioned the interest in evolving studies. What are they? What would you still like to know in this space?Sure, I think there's so much to know. So I I think I'm heartened by the fact that we know that this pedagogical practise of tutoring, individualising instruction really is the best way for anyone to learn anything. So if I were to take a piano, I could sit in a class, I could take a watch online YouTube videos, I might learn the piano. I'm much more likely to learn the piano in a way that I get better faster if I had a coach or a teacher come to my home and teach me one on one. Take what I already know from my, I don't know, 30 years ago piano tutorial experience, and then build on that and teach me in a consistent way. I think what we've never been able to figure out in public education is how do you take that pedagogical practise and make it so that you can fit it into the parameters of what the public education system can afford and how the system is set up where we, you know, typically have one teacher with 30 students. I think we have some evidence and and I would say that this study provides a proof point that technology can facilitate scale. I think what we don't really have are practical answers to policymakers questions about how do I do this in my own district? What is the first thing I stand up? How do I find these tutors? How do I make it so that they're allowed to be in a classroom with students, even legally without a teacher of record there? Or maybe with a teacher of record, allowing that teacher to, you know, be the sort of architect of a classroom where the the kids are getting some individualised instruction tutors, some are getting an individualised instruction from platforms. And then the teacher can use his or her pedagogical expertise to really work on the the more difficult problems of practise. That's the classroom of the future. I think we're still building towards that. And we don't know how to take all of these effective practises in their pockets and put them together so that they create a system that makes sense for students, you know, regardless of a creator background. And so from my vantage point, I want to understand what are those ratios of students to tutors at which you can't rely on technology anymore to facilitate scale? You know, can you have six kids? Can you have eight kids? Can you have 10 kids? Then you start to get into some of those classroom management problems that you had earlier. And I think a lot of the focus that we're, you know, really, really concentrating on for the next few years is what is the right mix of human capital and technology to facilitate that individualised instruction.
So I don't know the answer yet, but maybe I can come back in a few years. Thank you. I like that phrase. The the best mix of human capital and technology. One thing that did strike me from your paper was, was is there as the question of is there a sweet spot for the amount of tech technology used by either students in general or individual students? Could you say something about that?
Sure. So you know, I think during the pandemic, we saw there was too much technology. So the sort of like log on to Zoom for 8 hours a day and you know, have it teacher talk at you is not the kind of technology that seems to facilitate genuine learning opportunities. And what we hear a lot is, you know, kids are burnt out on technology. Now, if you think about the, the a different situation, maybe just prior to the pandemic, where we actually do see that technology helps kids learn. If you use the tech platform in the right way, it actually can help kids learn. And so if you're not using it enough, we're actually leaving learning on the table. We're we're using technology in the sub optimal way. And that's, you know, malpractice because we have all these students who are just begging to learn and we have very inequitable outcomes kind of not just within our our individual countries, but sort of globally that we need to kind of think about. So we can't afford to leave technology on the table or sorry, learning on the table that technology could facilitate that's opportunities and advancements that we are leaving on the table. And so I think what we're trying to do is find that sweet spot where, where it's the, uh, the optimal mix again, of both the human capital that you have at your disposal and then all also the, the technology and the technological modalities and platforms that you might be able to deploy. And something that I think is likely obvious to everybody in the audience, but should be stated is that I think that that right mix is highly context dependent. So if you're in a rural area, you might have to lean on the modality of technology more, but then you really need to think about how you're going to use your human capital to kind of overcome the Zoom fatigue that we saw during remote instruction. That might not be as true if you're in a more densely populated area, but you might have other challenges as well in terms of equitable, equitable distribution of that human capital and those opportunities. And so I think we just have to get a lot more granular, not just about what works on average, but sort of what are the overarching design principles as we've been talking about? And then how do you adapt them in your local context and still be confident that they're yielding the treatment effects that that we all want on student learning?
Well, thank you so much, Monica. My last question was going to actually be about why is, why is your study and why is technology more broadly important, particularly for students experiencing disadvantage and the schools that serve them? But I think your whole, everything you've had to say and share with us has been from that foundation of, of equity and needing to do to do better. And I think we all know that we can't leave any of the things on the table when we're trying to do that the the problems two pressing and to to large to overlook any of the potential levers. So that just leaves me to say thank you so much once again for your insights and for your time. Thanks, of course. Thank you for having me. Thank you.
Report: Towards high quality in Australian educational technology
To leverage the benefits of edtech for student learning, tools need to be well designed, effectively used, and appropriately governed, but in a rapidly growing sea of applications, how will we know when that is the case?
This report outlines how edtech increasingly is the mediating layer between curriculum and the classroom, highlighting the need for a national quality assurance process for educational technology (edtech) used in Australian classrooms.Report: Shaping AI and edtech to tackle Australia’s learning divide
Professor Loble produced this report during her tenure as a Paul Ramsay Fellow, affiliated with the UTS Centre for Social Justice & Inclusion. Establishing the Australian Network for Quality Digital Education enacts the report’s first recommendation, and the report's insights will underpin the Network’s priorities.
Submission: Review to inform a better and fairer education system
The Australian Government established the Review to Inform a Better and Fairer Education System as part of the process to develop the next National School Reform Agreement, to lift student outcomes across Australian schools. Professor Loble's submission to the review focuses on the potential for education technology to play a role in improving equity of student outcomes, providing certain conditions are met.
The Australian Government is investigating the use of generative artificial intelligence in the Australian education system. In addition to making a formal submission to the inquiry, Professor Loble presented expert evidence at a public hearing.
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ChatGPT in Lesson Preparation: A Teacher Choices Trial
Education Endowment Foundation
2024This trial examined the impact of ChatGPT on teacher workload and lesson preparation quality in secondary science education. Conducted as a cluster-randomised controlled trial, it involved 259 teachers across 68 schools in England, comparing those who used ChatGPT for lesson planning with those who did not.
Key findings:
- Teachers using ChatGPT spent 31 per cent less time on lesson preparation, saving an average of 25.3 minutes per week. Despite relatively modest AI use (mostly for generating quiz questions or activity ideas) teachers reported that it streamlined planning. Many used the saved time for other teaching tasks or to reduce their overall workload.
- An expert panel found no significant difference in the quality of lesson resources between the ChatGPT and non-GenAI groups.
- Teachers used ChatGPT less frequently over time, transitioning from an initial exploration phase to more selective use. Those with higher confidence in technology or lower confidence in science content were more likely to engage with the tool. While teachers valued ChatGPT’s efficiency, some expressed concerns about over-reliance and AI accuracy.
- ChatGPT can support lesson preparation, however, its effectiveness relied on structured implementation, to ensure that teachers understood ChatGPT’s capabilities and limitations.
About a quarter of U.S. teens have used ChatGPT for schoolwork – double the share in 2023
Pew Research Center
2025This report examines the growing use of ChatGPT among US teens for schoolwork, with adoption rates doubling since 2023. The findings highlight differences in usage by age, race, and awareness, as well as varying attitudes toward AI’s role in education.
Key takeaways:
- 26 per cent of U.S. teens (ages 13-17) now use ChatGPT for schoolwork, up from 13 per cent in 2023.
- Black and Hispanic teens (31 per cent) report higher usage than White teens (22 per cent). Usage rates remain consistent across gender and household income.
- Overall awareness has risen, with 79 per cent of teens now familiar with ChatGPT, up from 67 per cent in 2023. Awareness is higher among teens from wealthier households than lower-income households, though usage rates are consistent.
- Perceived acceptability varies by task:
- 54 per cent see it as appropriate for researching topics
- 29 per cent approve of using it for solving math problems
- Only 18 per cent find it acceptable to write essays.
Inclusive Intelligence: The Impact of AI on Education for All Learners
Center for Innovation, Design, and Digital Learning
2024This US-focused report provides a summary of the wide range of applications of AI in education and the opportunities and risks that each present. Issues canvased include the impact on assessment, tool design and integration, the roles of different stakeholders, and equity. It considers the impact of these emerging technologies on special education cohorts and research.
Key takeaways:
- Instructors can leverage AI to support curriculum development, lesson planning, personalised learning and classroom management, particularly benefiting special education programs.
- AI offers significant advantages for students with disabilities in offering new tools for personalised learning but requires careful management to avoid overreliance and resistance.
- Effective AI implementation requires open dialogue, collaboration and planning among educators, industry, parents and community to address challenges and maximise benefits.
- Ethical AI integration requires a balanced approach to ensure fairness and prevention of algorithmic bias and protect the privacy and security of children's data.
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
Michael Gerlich
2025This study investigates the impact of AI tools on critical thinking, focusing on cognitive offloading as a mediating factor. Using a mixed-method approach, it examines how reliance on AI affects cognitive engagement across different age groups and educational backgrounds. It highlights the cognitive trade-offs of AI tool reliance, emphasising the need for educational strategies that encourage active engagement with AI rather than passive dependence.
Key takeaways:
- Higher AI tool usage correlates with reduced critical thinking skills, as users increasingly delegate cognitive tasks to AI. This effect is seen the most among younger participants, who show lower engagement in analytical reasoning.
- Higher educational attainment is linked to better critical thinking skills, regardless of AI usage. Those with advanced degrees are more likely to critically evaluate AI-generated content rather than rely on it passively.
- Older participants (46+) engage in deeper cognitive processes and exhibit lower AI dependence. Younger users (17-25) rely more on AI tools, leading to increased cognitive offloading.
- Users who trust AI recommendations are more likely to accept AI-generated information without scrutiny. This trust reinforces cognitive offloading and reduces the need for independent problem-solving.
- Frequent reliance on AI tools reduces users’ ability to retain and recall information independently. This aligns with previous research on the ‘Google Effect’, where people remember where to find information rather than the information itself.
Generative AI in Education Hub
Scale Initiative, Stanford University
The Generative AI in Education Hub is a centralised repository for research, case studies, and resources exploring the use of artificial intelligence in educational settings. Hosted by Stanford’s Scale Initiative, the hub provides evidence-based insights into the opportunities and challenges of integrating AI in learning environments.
Key features:
- Research & Case Studies: The hub compiles studies on AI’s role in education, covering topics such as personalised learning, special needs support, assessment automation, and teacher assistance.
- Practical Implementation Guides: Resources help educators understand how to effectively apply AI tools in classrooms while considering ethical and pedagogical implications.
- Ethical & Policy Considerations: The hub includes studies that emphasise responsible AI use, addressing concerns such as bias, data privacy, and student well-being.
Empowering teaching and learning with educational technology
The Australian Council for Educational Research (ACER)
2025This literature review by the Australian Council for Educational Research (ACER) synthesises findings from 63 studies, examining how educational technology (edtech) is designed to improve teaching and learning and evaluating its impact on learning outcomes in school contexts. The report highlights that technology itself is not the driver of educational improvement – its effectiveness depends on conscious design, implementation, and integration with sound educational principles.
Key findings:
- Edtech can enhance student learning by supporting self-regulated learning (SRL), offering dashboards that provide real-time feedback and personalised learning pathways.
- Computer-supported collaborative learning (CSCL) tools improve teamwork and peer engagement by fostering knowledge-sharing and group awareness.
- Adaptive learning technologies help tailor content to individual student needs, improving engagement and comprehension.
- While educational technology has demonstrated positive impacts on learning, motivation, and engagement, challenges such as cognitive overload and inequitable access remain key concerns.
Digital Pathways for Education: Enabling Greater Impact for All
The World Bank
2024This report presents a framework for integrating digital solutions into education systems to enhance equitable access, learning outcomes, and system resilience. The report emphasises the importance of embedding digital technology into teaching and learning in a way that is strategic, scalable, and equitable.
Key findings:
- Equity and inclusion must be at the core of digital education strategies to ensure digital transitions do not exacerbate existing inequalities. This report calls for policy commitmentsto expand access to quality education for all, particularly for marginalised and underserved communities.
- Governments must invest in an integrated digital core that supports teaching, learning, and education management, rather than fragmented, digital ‘frills’.
- Stronger quality assurance mechanisms are needed to ensure that digital tools and platforms align with evidence-based educational practices. The report advocates for a shared set of global quality assurance standards to guide the selection and implementation of digital education solutions.
- The report highlights a disconnect between research and policymaking. There is a growing demand for robust, cost-effective, and scalable research to evaluate digital interventions, moving beyond ‘what works’ to also examine how, why, under what conditions, and for who digital solutions are effective.
- Strong governance and cross-sector collaboration are essential to scale digital transitions effectively. The report calls for whole-of-government and ecosystem-wide approaches, leveraging partnerships with the private sector, research institutions, and adaptive policymaking to scale digital transitions effectively.
ChatGPT in Lesson Preparation: A Teacher Choices Trial
Education Endowment Foundation
2024This trial examined the impact of ChatGPT on teacher workload and lesson preparation quality in secondary science education. Conducted as a cluster-randomised controlled trial, it involved 259 teachers across 68 schools in England, comparing those who used ChatGPT for lesson planning with those who did not.
Key findings:
- Teachers using ChatGPT spent 31 per cent less time on lesson preparation, saving an average of 25.3 minutes per week. Despite relatively modest AI use (mostly for generating quiz questions or activity ideas) teachers reported that it streamlined planning. Many used the saved time for other teaching tasks or to reduce their overall workload.
- An expert panel found no significant difference in the quality of lesson resources between the ChatGPT and non-GenAI groups.
- Teachers used ChatGPT less frequently over time, transitioning from an initial exploration phase to more selective use. Those with higher confidence in technology or lower confidence in science content were more likely to engage with the tool. While teachers valued ChatGPT’s efficiency, some expressed concerns about over-reliance and AI accuracy.
- ChatGPT can support lesson preparation, however, its effectiveness relied on structured implementation, to ensure that teachers understood ChatGPT’s capabilities and limitations.
Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration
U.S. Department of Education, Office of Educational Technology
2024
This toolkit offers strategic guidance to education leaders on incorporating AI into schools while safeguarding privacy, promoting equity, and ensuring ethical practices. It builds on the Department’s 2023 AI Report and is structured into three key sections:- Mitigating Risks (Modules 1-3)
Focuses on protecting student privacy, security, and civil rights. The section emphasises proactive risk management to maintain trust and transparency. - Building AI Strategies (Modules 4-7)
Provides a roadmap for integrating AI in the instructional core. The recommendations are intended to help educational leaders create coherent, evidence-based plans tailored to their communities. - Maximising Opportunities (Modules 8-10)
The final section focuses on optimising the use of AI through the development of AI literacy, updates to responsible use policies, and the creation of system-wide implementation plans.
The toolkit highlights the importance of aligning AI adoption with educational objectives, supporting equity, and maintaining human oversight in AI-driven decision-making processes.Digital equity and inclusion in education: An overview of practice and policy in OECD countries
OECD Education Working Paper No. 299
August 2023This paper explores the potential of digital technologies in OECD countries to enhance equity and inclusion, while noting that ongoing digital inequalities could worsen existing gaps. It emphasises the importance of equitable access, teacher capacity-building, and inclusive design.
Key takeaways:
- Personalised learning and assistive technologies can improve outcomes for students with special education needs, immigrant backgrounds, and socio-economically disadvantaged groups.
- 25 per cent of school leaders who participated in The OECD's Teaching and Learning International Study (TALIS) 2018 reported inadequate ICT resources as a major obstacle, and only half of 15-year-olds in OECD countries had access to an effective online learning platform pre-pandemic (pp.10-, 11).
- Over 30 per cent of principals report insufficient teacher training in digital pedagogy, highlighting the need for targeted professional development (p.10).
- Inclusive design is essential. Features like adaptive interfaces and assistive technologies can promote accessibility and support student well-being.
The paper calls for comprehensive policy frameworks and inclusive teaching strategies to maximise the potential of digital technologies while safeguarding equity and inclusion.
Center on Reinventing Public Education (CRPE)
2024This report draws on surveys and interviews with education school leaders to examines how teacher preparation programs in the U.S. are responding to the integration of AI in education.
Key takeaways:
- Schools of education are only beginning to implement AI-related training, often focusing on plagiarism prevention rather than broader pedagogical applications.
- Faculty interest and capacity remain significant barriers, with most faculty members lacking confidence in integrating AI into instruction.
- Leadership in education schools is generally optimistic about AI’s potential to enhance teaching and learning but acknowledges risks related to privacy, equity, and security.
The report highlights a need for a systemic shift in how AI is embedded into teacher preparation programs, with recommendations for policymakers, funders, and researchers to support these efforts.
Wicked Opportunities: Leveraging AI to Transform Education
Center on Reinventing Public Education (CRPE)
April 2024This report argues for the transformative potential of Artificial Intelligence (AI) in U.S. public education, emphasising the need for proactive leadership, equitable access, and systemic reform to address the challenges of the generative AI era. It highlights AI’s potential to either exacerbate inequities or drive meaningful change and provides a roadmap for integrating AI into education systems responsibly.
Key takeaways:
- AI offers opportunities to address widening achievement gaps, pandemic recovery, a low supply of quality teachers, rising mental health challenges, and other ‘wicked problems’ without clear solutions (p.2).
- AI adoption is uneven, with suburban districts twice as likely to provide AI training as urban or rural schools (p.5). Distrust in AI persists, with educators citing concerns about its effectiveness and impact on student data.
- The current policy landscape is fragmented, requiring federal and state frameworks to address potential bias, resolve access and resource disparities, maintain data privacy, determine AI quality, and ensure AI literacy.
The report concludes with a short-term action plan focused on building AI literacy, piloting research-driven programs, and redesigning education systems to emphasise durable skills and equitable access.
Designing for Education with Artificial Intelligence: An Essential Guide for Developers
Office of Educational Technology, U.S. Department of Education
2024This guide responds to the increasing incorporation of AI models and capabilities into products serving educational settings. This guide seeks to help educational technology developers work toward safety, security and trust when creating AI products and services for use in education. It draws on extensive consultation with students, parents, and educators along with developers, industry associations, and nonprofit organisations.
The report recommends:
- Designing for teaching and learning
- Providing evidence for rationale and impact
- Advancing equity and protecting civil rights
- Ensuring safety and security
- Promoting transparency and earning trust.
This guide is more recent than the 2015 Office of Educational Technology guide but the insights and recommendations from the earlier document remain relevant. It can also be found in the repository.
How Dare They Peep into My Private Life?
Human Rights Watch
2022The Human Rights Watch report investigates the use of educational technology (edtech) endorsed by 49 governments during the COVID-19 pandemic, revealing significant violations of children's privacy. Of the 163 edtech products analysed, 89 per cent engaged in data practices that risked or directly infringed on children's rights, often without their knowledge or consent.
Key takeaways:
- Many edtech platforms secretly monitored children’s online activities, tracking their locations, interactions, and behaviours both inside and outside the classroom, often sharing this data with third-party companies.
- Governments failed to adequately vet these technologies, exposing children to the risk of data exploitation and surveillance without their or their parents' informed consent.
- The report emphasises the urgent need for stronger data protection laws and accountability measures to safeguard children's rights in the digital learning environment.
Australian Framework for Generative AI in Schools
Commonwealth of Australia
2023The Australian Framework for Generative AI provides guidance on understanding, using and responding to generative AI in Australian school‑based education. The framework aims to recognise how the appropriate use of generative AI tools can enhance teaching and learning outcomes while safeguarding ethical practices, privacy, and equity.
Key takeaways:
- Generative AI should be used to support teaching and learning, enhancing critical thinking and creativity without compromising academic integrity.
- The framework highlights the importance of protecting student data, ensuring compliance with privacy laws, and implementing robust cybersecurity measures.
- Equity and inclusion are prioritised, with a focus on making AI tools accessible and fair for all students, particularly those in rural and remote communities.
- The framework underscores the necessity of continuous monitoring and accountability, with school leaders retaining control over AI-supported decisions.
Guidance for generative AI in education and research
UNESCO
2023UNESCO's guidance takes a humanistic approach to assessing the risks as well as potential advantages of generative AI in education. It includes an accessible introduction to generative AI and how it works, as well as to the key controversies surrounding it, and a number of possible use cases.
Key education takeaways include:
- Generative AI is currently unreliable, understanding 'neither the prompt nor the response,' and generative content purely on the basis of 'probabilities of language patterns' (p.26). Young learners are at particular risk of accepting output at face value.
- The implications of generative AI for knowledge creation, transmission and validation are still emerging, but foundational knowledge and skills, and higher-order thinking skills, will remain critical to ensure that humans can critically evaluate and engage with AI output.
- Any generative AI-based applications used in education should be 'educationally effective and valid for the ages and abilities of the target learners, and … aligned with sound pedagogical principles (i.e. based on the relevant domains of knowledge and the expected learning outcomes and development of values)' (p.25).
- Human agency, including the intrinsic motivation to learn, should be safeguarded.
- Teachers 'need to be supported to strengthen their capacities for the proper use of GenAI' (p.20).
Data Equity: Foundational Concepts for Generative AI Briefing Paper
World Economic Forum
2023The World Economic Forum's briefing paper unpacks the concept of data equity -- 'a core notion within data governance centred on the impact of data on the equity of technical systems for individuals, groups, enterprises and ecosystems' (p.3) -- in the context of large language (also known as foundation) models. The paper identifies key equity considerations across the data lifecycle, specifically:
- Input data equity (the importance of accurate representation of diverse communities within training data, noting the 'intricate trade-offs' between representation and privacy concerns)
- Algorithmic data equity (the importance of ensuring that algorithms function as impartially and accurately as possible for all populations, noting the inherent challenges foundation models pose to algorithmic transparency, and the importance of AI literacy for understanding models; capabilities and limitations)
- Output data equity (the fairness of the models' tangible effects, noting the importance of equitable distribution of benefits).
Centre for Evidence and Implementation
2023Singapore has focused strongly on the integration of technology across society, including education, for at least two decades. Following COVID, 'Singapore is transitioning to utilising technology in business-as-usual education delivery.' Technology is approached as a key lever for meeting the needs of the full range of learners, with a particular focus on students with disability. Nonetheless, Singapore faces a range of familiar challenges in seeking consistent, skillful implementation.
Key takeaways:
- Students can use the Singapore Learning System at any time, to study any subject at any level and have their progress monitored. Teachers also integrate resources from this platform into their programs. Usage rates of 80% students and 70% teachers are reported.
- Student engagement concerns persist, and screen fatigue may be an issue.
- Teaching practices that integrate pedagogy and technology to create cognitive scaffolding for lower-progress students have been shown to impact positively on student self-efficacy and motivation to learn.
- Familiar challenges include: challenges in identifying, evaluating or curating high-quality content; pace of change; balancing the positive impact of technology with the downside of technological distractions for students; navigating variability in teacher attitudes to, confidence and competence with, technology; ensuring equity of access (though this issue appears to be on a lesser scale than other countries).
EdTech Developer's Guide: A primer for software developers, start-ups and entrepreneurs
Office of Educational Technology, U.S. Department of Education
2015Despite being a few years old, this guide's identification of key opportunities for technology to support and improve teaching and learning still holds true. The guide draws significantly on teacher and educator perspectives, reinforcing the value of putting user perspectives at the heart of product design and development. Identified opportunities are:
- Improving mastery of academic skills
- Developing skills to promote lifelong learning
- Increasing family engagement
- Planning for future education opportunities
- Designing effective assessments
- Improving educator professional development
- Improving educator productivity
- Making learning accessible to all students
- Closing opportunity gaps
- Closing achievement gaps.
High-Dosage Tutoring at Scale: Evidence from a Cost-Effective, Blended-Learning Tutoring Model
University of Chicago Education Lab
2023This study, in partnership with Chicago Public Schools, New York City Department of Education, and Saga Education, finds that incorporating an online mathematics application can effectively scale the benefits of in-person, small-group tutoring at reduced per-student cost. Students alternated between working directly with a tutor and working with an educational application. Participation in the hybrid model resulted in 1-2 years of additional mathematics learning, comparable with the results achieved by the original Saga model.
Stanford University students and personnel with U.S. high school teachers
The goal of the CRAFT program is to provide free, high-quality resources informed by learning sciences research for non-profit use by classroom educators.
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- A year in review: 2024
- How can we harness the power of edtech?
- Australia needs quality assurance to harness benefits of AI and edtech for students and schools
- EdTech is booming. But is it any good?
- The rise of ChatGPT shows why we need a clearer approach to technology in schools
- New EdTech network to address educational inequality
- Stronger governance of edtech needed
