Education with AI, for AI or about AI? Positioning Large Language Models in Learning with Aligned Values

Author – Xiaoyu Wang and Tuan-Ting Huang

As the academic field increasingly implements technology to assist with output, LLMs have moved to centre stage, proving they can serve as powerful co-pilots, assisting with understanding abstract concepts, ideation, language-based prototyping, documentation and communication across disciplines. In educational settings, particularly with design students, LLMs have great potential as Creativity Support Tools (Frich Pedersen et al., 2018) and design material (Yu, 2025). They can help expand students’ creative thinking, expose them to diverse perspectives and scaffold the exploration of ideas. LLMs can also act as a bridge between design and other fields, facilitating interdisciplinary learning and collaboration. As for younger students, LLMs are like smart, friendly partners in classrooms that adapt to each student’s pace and make the learning more interactive and less intimidating. They can offer personalised explanations, generate engaging exercises and provide constructive feedback, enhancing students’ confidence and autonomy. As education systems continue to embrace emerging technologies, LLMs can play a key role in making learning more accessible, personalised and creatively enriching for all learners (Casal-Otero et al., 2023).

Explainability and Fairness: Building Trust and Agency

Many learners, especially youth and design students, are encountering AI for the first time or are still building foundational understanding. In this context, explainability is essential. It enables students to understand how AI works, increases their confidence in using it and fosters more critical and thoughtful engagement with these tools. By introducing LLMs early, we help plant the seeds of curiosity and critical thinking (Vasquez & Felderman, 2012). Rather than framing AI as mysterious or opaque, we should frame it as something understandable—something that can be questioned, tinkered with and used creatively. Explainability gives students a compass: a sense of direction and agency, rather than a static set of instructions.

Fairness is equally important, as it shapes how students learn to evaluate and question the societal impacts of AI. Especially for younger students, whose brains are still developing, fairness ensures every student feels seen, supported and valued. It also helps create a learning environment where all students get access to the same quality of support and information, regardless of their background, language proficiency or learning style. In this way, fairness not only influences individual learning outcomes but also contributes to building a more just and empathetic society. Embedding these principles into their education empowers them to navigate future technological developments with autonomy, agility and a critical lens.

Aligning with Core Educational Values

In educational contexts, especially for younger students: values like creativity, curiosity and critical thinking are as crucial as developing skills and competency related to knowledge and technologies. These values shape not only what students learn, but how they learn, interact and participate in an increasingly complex, AI-mediated world. Thus, besides explainability and fairness mentioned earlier, creativity and critical thinking should also be included as the main features to align LLMs with human values.

Creative LLMs can help students explore ideas and express themselves in diverse and meaningful ways. When students are invited to imagine and create within supportive, exploratory environments, learning becomes more engaging, relevant and personally resonant. This is where the perspective of design education offers valuable insights. In higher education design programmes, creative exploration, iteration and reflection are central to learning.

We should aim to draw from these pedagogical models and bridge them with constructivist approaches and tangible tools. The goal is not to transplant higher education methods entirely, but to translate their core principles, such as open-ended exploration, critique and co-creation, into appropriate forms that resonate with younger learners.

Building on a foundation of creativity, critical thinking enables students to become active participants rather than passive consumers of technology. It encourages them to challenge and reflect on the outputs LLMs generate, and to see uncertainty not as failure, but as an invitation for intervention. In doing so, students develop a more rigorous and empowered relationship with technology and its role in shaping knowledge.

By examining these values across different educational contexts, student groups and methodologies, central values and features can be defined and a set of core, actionable principles can be developed. These principles can then guide how we design, evaluate and implement AI tools in classrooms and ensure that these technologies are not only responsibly deployed but also support students to grow into creative, confident and ethically engaged learners towards applying AI in their studies and personal development.

References:

Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1), 29. https://doi.org/10.1186/s40594-023-00418-7.

Frich Pedersen, J., Biskjaer, M. M., & Dalsgaard, P. (2018). Why HCI and creativity research must collaborate to develop new Creativity Support Tools. Proceedings of the Technology, Mind & Society Conference, Article 10. Association for Computing Machinery. https://doi.org/10.1145/3183654.3183678.

Kennedy, T.J., Sundberg, C.W. (2020). 21st Century Skills. In: Akpan, B., Kennedy, T.J. (eds) Science Education in Theory and Practice. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-43620-9_32.

Vasquez, V.M., & Felderman, C.B. (2012). Technology and Critical Literacy in Early Childhood (1st ed.). Routledge. https://doi.org/10.4324/9780203108185.

Yu, W. F. (2025). AI as a co‑creator and a design material: Transforming the design process. Design Studies, 97, 101303. https://doi.org/10.1016/j.destud.2025.101303.

Further reading/watching/listening:

Books & Articles:

Lindrup, M., Jacobsen, R. M., Wester, J., van Berkel, N., Raptis, D., & Nielsen, P. A. (2025). Prompt Machine: A tangible generative AI tool for supporting children’s learning and literacy. In Proceedings of the 2025 ACM Designing Interactive Systems Conference (DIS ’25) (pp. 489–505). Association for Computing Machinery. https://doi.org/10.1145/3715336.3735673.

Videos & Podcasts:

Educating Kids in the Age of A.I., The Ezra Klein Show (2025) https://youtu.be/HQQtaWgIQmE?si=L5Y1Iq9H3y1eUt3-.