Hanna Barakat + AIxDESIGN & Archival Images of AI / Better Images of AI / Data Mining 3 / CC-BY 4.0
Every day hundreds of posts are written on social media about AI and education. Every day yet more papers are published about AI and education. Webinars, seminars and conferences about AI and education. Yet nearly all of them are about formal education, education in the classroom. But as Stephen Downes says in a commentary on a blog by Alan Levine we need more on how people actually teach and actually learn. "We get a lot in the literature about how it happens in the classroom. But the classroom is a very specialized environment, designed to deal with the need to foster a common set of knowledge and values on a large population despite constraints in staff and resources. But if we go out into homes or workplaces, we see teaching and learning happening all the time..."
And of course people learn in different ways - through being showed how to do something, through watching a video, through working, playing and talking. Sadly in all these discussions about AI and education there is little about how people learn and even less on how AI might support (or hinder) informal learning.
In his paper, Mike Sharples explores the evolving landscape of generative AI in education by discussing different AI system approaches. He identifies several potential AI types that could transform learning interactions: generative AIs that act as possibility generators, argumentative opponents, design assistants, exploratory tools, and creative writing collaborators.
The research highlights that current AI systems primarily operate through individual prompt-response interactions. However, Sharples suggests the next significant advancement will be social generative AI capable of engaging in broader, more complex social interactions. This vision requires developing AI with sophisticated capabilities such as setting explicit goals, maintaining long-term memory, building persistent user models, reflecting on outputs, learning from mistakes, and explaining reasoning.
To achieve this, Sharples proposes developing hybrid AI systems that combine neural networks with symbolic AI technologies. These systems would need to integrate technical sophistication with ethical considerations, ensuring respectful engagement by giving learners control over their data and learning processes.
Importantly, the paper emphasizes that human teachers remain fundamental in this distributed system of human-AI interaction. They will continue to serve as conversation initiators, knowledge sources, and nurturing role models whose expertise and human touch cannot be replaced by technology.
The research raises critical philosophical questions about the future of learning: How can generative AI become a truly conversational learning tool? What ethical frameworks should guide these interactions? How do we design AI systems that can engage meaningfully while respecting human expertise?
Mike Sharples concludes by saying that designing new social AI systems for education requires more than fine tuning existing language models for educational purposes.
It requires building GenAI to follow fundamental human rights, respect the expertise of teachers and care for the diversity and development of students. This work should be a partnership of experts in neural and symbolic AI working alongside experts in pedagogy and the science of learning, to design models founded on best principles of collaborative and conversational learning, engaging with teachers and education practitioners to test, critique and deploy them. The result could be a new online space for educational dialogue and exploration that merges human empathy and experience with networked machine learning.
Yutong Liu & The Bigger Picture / Better Images of AI / AI is Everywhere / CC-BY 4.0
Graham Attwell, George Bekiaridis and Angela Karadog have written a new paper, AI and Education: Agency, Motivation, Literacy and Democracy. The paper has been published as a preprint for download on the Research Gate web site.
This is the abstract.
This paper, developed as part of the research being undertaken by the EU Erasmus+ AI Pioneers project, examines the use of generative AI in educational contexts through the lens of Activity Theory. It analyses how the integration of large language models and other AI-powered tools impacts learner agency, motivation, and AI literacy. The authors conducted a multi-pronged research approach including literature review, stakeholder interviews, social media monitoring, and participation in European initiatives on AI in education. The paper highlights key themes around agency, where AI can both support and challenge learner autonomy depending on how the technology is positioned and implemented. It explores the complex relationships between AI, personalization, co-creation, and scaffolding in fostering student agency. The analysis also examines the effects of generative AI on both intrinsic and extrinsic motivation for learning, noting both opportunities and potential pitfalls that require careful consideration by educators. Finally, the paper argues that developing critical AI literacy is essential, encompassing the ability to understand AI capabilities, recognize biases, and evaluate the ethical implications of AI-generated content. It suggests that a broader, more democratic approach to curriculum and learning in vocational education and training is necessary to empower students as active, informed citizens in an AI-driven future. The findings provide an approach to the complex interplay between generative AI, learner agency, motivation, and digital literacy in educational settings, particularly in the context of vocational education and adult learning.
In last weeks edition of her newsletter, Philippa Hardman reported on an interesting research project she has undertaken to explore the effectiveness of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini in instructional design. It seems instructional designers are increasingly using LLMs to complete learning design tasks like writing objectives, selecting instructional strategies and creating lesson plans.
The question Hardman set out to explore was: “how well do these generic, all-purpose LLMs handle the nuanced and complex tasks of instructional design? They may be fast, but are AI tools like Claude, ChatGPT, and Gemini actually any good at learning design?” To find this out she set two research question. The first was sound the Theoretical Knowledge of Instructional Design by LLMs and the second to assess their practical application.She then analysed each model’s responses to assess theoretical accuracy, practical feasibility, and alignment between theory and practice.
In her newsletter Hardman gives a detailed account of the outcomes of testing the different models from each of the three LLM providers, But the The headline is that across all generic LLMs, AI is limited in both its theoretical understanding and its practical application of instructional design. The reasons she says is that they lack industry specific knowledge and nuance, they uncritically use outdated concepts and they display a superficial application of theory.
Hardman concludes that “While general-purpose AI models like Claude, ChatGPT, and Gemini offer a degree of assistance for instructional design, their limitations underscore the risks of relying on generic tools in a specialised field like instructional design.”
She goes on to point out that in industries like coding and medicine, similar risks have led to the emergence of fine-tuned AI copilots, such Cursor for coders and Hippocratic AI for medics and sees a need for “similar specialised AI tools tailored to the nuances of instructional design principles, practices and processes.”
In last weeks edition of her newsletter, Philippa Hardman reported on an interesting research project she has undertaken to explore the effectiveness of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini in instructional design. It seems instructional designers are increasingly using LLMs to complete learning design tasks like writing objectives, selecting instructional strategies and creating lesson plans.
The question Hardman set out to explore was: “how well do these generic, all-purpose LLMs handle the nuanced and complex tasks of instructional design? They may be fast, but are AI tools like Claude, ChatGPT, and Gemini actually any good at learning design?” To find this out she set two research question. The first was sound the Theoretical Knowledge of Instructional Design by LLMs and the second to assess their practical application.She then analysed each model’s responses to assess theoretical accuracy, practical feasibility, and alignment between theory and practice.
In her newsletter Hardman gives a detailed account of the outcomes of testing the different models from each of the three LLM providers, But the The headline is that across all generic LLMs, AI is limited in both its theoretical understanding and its practical application of instructional design. The reasons she says is that they lack industry specific knowledge and nuance, they uncritically use outdated concepts and they display a superficial application of theory.
Hardman concludes that “While general-purpose AI models like Claude, ChatGPT, and Gemini offer a degree of assistance for instructional design, their limitations underscore the risks of relying on generic tools in a specialised field like instructional design.”
She goes on to point out that in industries like coding and medicine, similar risks have led to the emergence of fine-tuned AI copilots, such Cursor for coders and Hippocratic AI for medics and sees a need for “similar specialised AI tools tailored to the nuances of instructional design principles, practices and processes.”
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