The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education

Synthesis: Hackl, Müller, and Sailer (2026) present the AI Literacy Heptagon, a structured seven-dimensional framework for AI literacy (AIL) in higher education, developed through an integrative literature review of publications from 2021–2024. The framework synthesizes seven core dimensions — technical knowledge and skills, application proficiency, critical thinking ability, ethical awareness and reasoning, social impact understanding, integration skills, and legal and regulatory knowledge — and is operationalized through four Bloom's-taxonomy-aligned proficiency levels (Unaware, Beginner, Intermediate, Expert). An initial expert-led curriculum mapping across an AI Engineering and a Media Pedagogy program demonstrated the framework's utility for analyzing and structuring curricula, highlighting the discipline-specific nature of AIL implementation.

Key Findings

Study Design & Method

The study used an integrative literature review (distinct from a meta-analysis, as it performs qualitative synthesis rather than statistical pooling), following PRISMA principles for transparency. A systematic search of Web of Science and Scopus for English-language publications from 2021 (plus earlier foundational works) to 2024 used term combinations around AIL, higher education, teaching/learning AI, and stakeholders. The search was completed December 10, 2024, with additional sources via citation tracking, Google Scholar, and research discovery tools. Two independent coders applied open then axial coding to extract recurring competencies, resolving discrepancies through discussion, yielding the seven dimensions. Initial framework validation used structured expert-led "collaborative profiling sessions" with two program leaders (AI Engineering and Media Pedagogy) to map curricula onto the heptagon and its proficiency levels — explicitly framed as evaluating curricular structure and goals, not empirically measuring student competencies.

Implications for AI in Education

The Heptagon provides educators and institutions a concrete, adaptable tool for structuring AI Literacy development in Higher Ed, addressing the field's fragmentation and the gap between conceptual definitions and curriculum implementation. It argues that AIL is not a monolithic competency but must be tailored to disciplinary contexts while maintaining seven core dimensions. The explicit inclusion of legal and regulatory knowledge and integration skills responds to underrepresented dimensions in existing frameworks and to the fast-moving regulatory landscape. For curriculum designers, the framework supports competency-gap analysis (as demonstrated in the two validation programs), the design of targeted learning modules across dimensions and proficiency levels, and assessment tasks that integrate multiple AIL dimensions. It also situates AIL relative to media, computational, and data literacy, helping delineate what is and is not AI literacy.

Limitations

The literature review is bounded to 2021–2024 English-language publications in Web of Science and Scopus, potentially missing relevant work. The initial validation is qualitative and small-scale — two expert-led curriculum mappings — explicitly framed as illustrating the framework's utility rather than empirically validating its effectiveness or measuring student competencies. The authors note the visualization lacks granularity at the expert level and that the knowledge-skills-attitudes three-dimensional nature is not explicitly represented in the visual model. The framework is a proposed instrument requiring further validation across more programs and disciplines.

Connected Concepts

Connected Articles

Citation

Hackl, V., Müller, A. E., & Sailer, M. (2026). The AI literacy heptagon: A structured approach to AI literacy in higher education.