AI Ed Wiki logoAI Ed Wiki
Use with AI

📄 Research Article

Towards AI literacy: A proposal of a framework based on the Episodes of Situated Learning

● high · 2026-08-16 · ai-literacy, situated-learning, curriculum-design, instructional-design, k-12, experiential-learning, generative-ai

Synthesis: Panciroli, Allegra, Gentile & Rivoltella propose a framework for curricular AI literacy integration built on the Episode of Situated Learning (ESL) instrument. ESL is an active teaching device organized around three verbs — anticipate, produce, reflect — that operates at both the micro level (lesson planning) and macro level (curriculum construction). The authors situate AI literacy within the broader landscape of new literacies (media, information, data, AI), draw on European AI competence frameworks (EU Digital Education Action Plan, de la Higuera's five pillars, DigComp 2.2), and distinguish direct didactics of AI (learning how AI works) from indirect teaching of AI (learning to collaborate with AI). Both levels fall under an educational mode of "explainability" centred on social interaction and school-community participation.

AI literacy within the new literacies landscape

The paper situates AI education within the broader evolution of literacy concepts, following the New London Group's multiliteracy framework. It distinguishes at least four specific literacies with distinct competences: media literacy (comprehension of media messages), information literacy (finding, analysing, using, sharing information), data literacy (understanding the reality of data and developing reflexive, critical interaction with it), and AI literacy (knowing, understanding, using, applying, evaluating, and creating AI). AI literacy thus sits within a family of related, mutually-reinforcing literacies rather than as a standalone skill.

European AI competence frameworks

The proposal is anchored in existing European frameworks:

  • The EU Digital Education Action Plan (2021-2027) identifies digital skills for "literacy, including combating misinformation, teaching computer literacy, [and] knowledge of data-intensive technologies."
  • de la Higuera's five pillars for building an AI curriculum: (1) Data Awareness, (2) Uncertainty and Randomness, (3) Coding and Computational Thinking, (4) Critical Thinking, and (5) Post-AI Humanism — the idea that AI forces reconsideration of fundamental truths about the human being (Truth, Experience, Creativity, Intelligence).
  • DigComp 2.2 (Italian Digital Agency, 2022), which addresses new and emerging AI technologies including personal data, interaction with AI systems, IoT, environmental sustainability, new forms of work, virtual/augmented reality, and robotisation.

Two levels of AI in education

The paper distinguishes two ways AI enters teaching:

  • Direct didactics of AI — fostering knowledge of AI definitions, vocabulary, fields of application, and modes of operation; essentially building AI literacy so students use these technologies consciously (e.g., learning how machine learning works, how AI is trained, how data is used).
  • Indirect teaching of AI — leading students to recognize and use AI applications to solve a problem or achieve a goal, learning to collaborate with AI to improve the teaching/learning process.

Both levels operate under the educational mode of Explainability, in which a teaching activity is designed within a soliciting educational environment centred on social interactions, initiating a process of "AI culture" in the school community.

The Episode of Situated Learning (ESL) instrument

ESL is an active teaching instrument built on three verbs — anticipate (students elaborate initial pre-knowledge and form hypotheses), produce, and reflect — that functions at the micro level (lesson planning) and macro level (curriculum construction). The authors propose declining these two levels for developing an AI Education curriculum. ESL is circumscribed temporally and in content (its "episode" nature, consistent with microlearning logic), begins with a preparatory phase enabling cognitive anticipation, and supports autonomous questioning and hypothesis-formation.

Educational significance

This paper connects AI literacy to situated learning theory as a curricular design strategy. It positions AI competence as something to be developed through active, situated episodes rather than abstract instruction, bridging technical and non-technical disciplines. The "Post-AI Humanism" pillar links to the broader philosophy of AI in education discussion, while the direct/indirect distinction and explainability mode connect to Instructional Design and K-12 AI education.

Key Findings

  • Proposes a curricular AI literacy framework built on the Episode of Situated Learning (ESL) instrument (anticipate, produce, reflect), usable at micro (lesson) and macro (curriculum) levels.
  • Situates AI literacy within the multiliteracy landscape: media, information, data, and AI literacy.
  • Grounds the framework in European AI competence frameworks (EU Digital Education Action Plan, de la Higuera's five pillars, DigComp 2.2).
  • Distinguishes direct didactics of AI (learning how AI works) from indirect teaching of AI (learning to collaborate with AI), both under an "explainability" mode.
  • Emphasizes the Post-AI Humanism pillar — AI prompting reconsideration of human truths (truth, experience, creativity, intelligence).
  • Argues for interdisciplinary bridging between technical and non-technical disciplines.

Connected Concepts

Connected Articles

Citation

Panciroli, C., Allegra, M., Gentile, M., & Rivoltella, P. C. (2023). Towards AI literacy: A proposal of a framework based on the Episodes of Situated Learning. CEUR Workshop Proceedings (Ital-IA 2023). doi:10.2760/115376.