Public Domain Data
Jamillah Knowles / Better Images of AI / Data People / CC-BY 4.0
I’ve been looking at how we could use Open Source software to develop Generative AI applications for education. Of course one of the issues is data for…
Wales Wide Web
Social generative AI for education
I am very impressed with a paper, Towards social generative AI for education: theory, practices and ethics, by Mike Sharples. Here is a quick summary but I recommend to read the entire article.
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.
Do we need specialised AI tools for education and instructional design?
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.”
Do we need specialised AI tools for education and instructional design?
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.”
What is the purpose of Vocational Education and Training?
Is Artificial Intelligence challenging us to rethink the purpose of Vocational Education and Training? Perhaps that is going too far, but there are signs of questions being asked. For the last twenty five years or so there has been a tendency in most European countries for a narrowing of the aims of VET, driven by an agenda of employability. Workers have become responsible for their own employability under the slogan of Lifelong Learning. Learning to learn has become a core skill for students and apprentices, not to broaden their education but rather to be prepared to update their skills and knowledge to safeguard their employability.
It wasn’t always so. The American philosopher, psychologist, and educational reformer John Dewey believed “the purpose of education should not revolve around the acquisition of a pre-determined set of skills, but rather the realization of one's full potential and the ability to use those skills for the greater good.” The overriding theme of Dewey's work was his profound belief in democracy, be it in politics, education, or communication and journalism and he considered participation, not representation, the essence of democracy.
Faced with the challenge of generative AI, not only to the agency and motivation of learners, but to how knowledge is developed and shared within society, there is a growing understanding that a broader approach to curriculum and learning in Vocational Education and Training is necessary. This includes a more advanced definition of digital literacy to develop a critique of the outputs from Large Language Models. AI literacy is defined as the knowledge and skills necessary to understand, critically evaluate, and effectively use AI technologies (Long & Magerko, 2020) including understanding the capabilities and limitations of AI systems, recognising potential biases and ethical implications of AI-generated content and developing critical thinking skills to evaluate AI-produced information .
UNESCO says their citizenship education, including the competence frameworks for teachers and for students, builds on peace and human rights principles, cultivating essential skills and values for responsible global citizens. It fosters criticality, creativity, and innovation, promoting a shared sense of humanity and commitment to peace, human rights, and sustainable development. Fenchung Miao from UNESCO has said the AI competency framework for students proposed the term of "AI society citizenship" and provided interpretation in multiple sections. Section 1.3 of the Framework, AI Society Citizenship says:
Students are expected to be able to build critical views on the impact of AI on human societies and expand their human centred values to promoting the design and use of AI for inclusive and sustainable development. They should be able to solidify their civic values and the sense of social responsibility as a citizen in an AI society. Students are also expected to be able to reinforce their open minded attitude and lifelong curiosity about learning and using AI to support self actualisation in the AI era.
The Council of Europe says Vocational Education and Training is an integral part of the entire educational system and shares its broader aim of preparing learners not only for employment, but also for life as active citizens in democratic societies. Social dialogue and corporate social responsibility are seen as tools for democratising AI in work.
Renewing the democratic and civic mission of education underlines the importance of integrating Competences for Democratic Culture (CDC) in VET to promote quality citizenship education. This initiative aims to support VET systems in preparing learners not only for employment but also for active participation as citizens in culturally diverse democratic societies. By embedding CDC in learning processes in VET, the Council of Europe aims to ensure that VET learners acquire the necessary knowledge, skills, values and attitudes to participate fully in democratic life.
The Council of Europe Reference Framework for Democratic Culture and the Unesco AI Competence Framework can provide a focus for a wider understanding of AI competences in VET and provide a challenge for how they can be implemented in practice.
Such an understanding can shape an educational landscape that leverages AI while safeguarding human agency, motivation, and ethics. As generative AI advances, continuous dialogue and investigation among all educational stakeholders are essential to ensure these technologies enhance learning outcomes and equip students for an AI-driven future.
References
Dewey, J. (1916) Democracy and Education: an introduction to the philosophy of education, New York: Macmillan. https://archive.org/stream/democracyandedu00dewegoog#page/n6/mode/2up. Retrieved 4 May 2024
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
UNESCO (2024) AI Competency Framework for Students, https://unesdoc.unesco.org/ark:/48223/pf0000391105