Study 01
Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023). International Journal of Educational Technology in Higher Education, 20, 43.
Student perceptionsStudentsEmpirical
What this adds
Shows students value immediacy, writing, research, and personalized support while worrying about accuracy, transparency, privacy, ethics, career effects, and unclear policies.
Why it matters
Student AI literacy support cannot be designed around academic integrity alone. Students need clear policy, conceptual understanding, ethical guidance, and help preserving higher-order skills.
Open source / DOI ↗Study 02
Exploring generative AI literacy in higher education: Student adoption, interaction, evaluation and ethical perceptions
Chen, K., Tallant, A. C., & Selig, I. (2024). Information and Learning Sciences, 126(1/2), 132–148.
Student practicesStudentsEmpirical
What this adds
Examines adoption and interaction alongside evaluation and ethical perceptions rather than treating use frequency as literacy.
Why it matters
Supports the distinction between being a frequent AI user and being able to evaluate, verify, and use AI responsibly.
Open source / DOI ↗Study 03
A qualitative study of students’ lived experience and perceptions of using ChatGPT: Immediacy, equity and integrity
Holland, A., & Ciachir, C. (2025). Interactive Learning Environments, 33(1), 483–494.
Equity & integrityStudentsQualitative
What this adds
Finds that immediacy, equity, and integrity shape students’ lived experience. Students valued reassurance and creativity but raised concerns about unequal access, group-work misconduct, and unclear institutional rules.
Why it matters
Highlights why student voice and policy transparency belong inside AI literacy, not outside it.
Open source / DOI ↗Study 04
University students’ conceptualisation of AI literacy: Theory and empirical evidence
Cerný, M. (2024). Social Sciences, 13, 129.
Defining AI literacyStudentsEmpirical
What this adds
Shows that students’ mental models of AI literacy include more than tool competence and helps expose incomplete understandings of how AI systems work.
Why it matters
Conceptual misunderstanding can undermine prompting, evaluation, source checking, and judgments about appropriate use.
Open source / DOI ↗Study 05
Context matters: Understanding student usage, skills, and attitudes toward AI to inform classroom policies
Cahill, C., & McCabe, K. (2024). PS: Political Science & Politics, 57(4).
Policy & contextStudentsEmpirical
What this adds
Connects students’ actual use, skills, and attitudes to classroom policy rather than assuming one policy fits every course.
Why it matters
Supports situated AI literacy: expectations should reflect task, discipline, learner practice, and instructional purpose.
Open source / DOI ↗Study 06
The AI generation gap: Are Gen Z students more interested in adopting generative AI than their Gen X and millennial teachers?
Chan, C. K. Y., & Lee, K. K. W. (2023). Smart Learning Environments, 10, 60.
Faculty & studentsStudents + facultyEmpirical
What this adds
Documents differences in interest and adoption across student and teacher groups.
Why it matters
Faculty development cannot assume instructors and students begin from the same experiences, confidence, or expectations.
Open source / DOI ↗Study 07
A systematic literature review of generative artificial intelligence literacy in schools
Park, J. (2025). Computers and Education: Artificial Intelligence, 9, 100487.
GenAI literacyStudentsSystematic review
What this adds
Synthesizes 51 empirical studies into five GenAI literacy competencies and identifies recurring problems with prompting, evaluation, privacy, agency, and ethical use.
Why it matters
Provides one of the clearest evidence syntheses showing that evaluation must be explicitly taught and scaffolded.
Open source / DOI ↗Study 08
Evaluating AI literacy in academic libraries: A survey study with a focus on U.S. employees
Lo, L. S. (2024). College & Research Libraries, 85(5), 635.
LibrariesLibrary employeesEmpirical survey
What this adds
Surveyed 760 academic library employees and found limited readiness, uneven access to professional development, and strong demand for deeper training in AI concepts, applications, privacy, and data security.
Why it matters
Shows that libraries can be AI-literacy partners while also needing equitable professional learning across staff roles.
Open source / DOI ↗Study 09
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023). International Journal of Educational Technology in Higher Education, 20, 38.
Institutional policyStudents + faculty + staffMixed methods
What this adds
Frames institutional AI response through pedagogical, governance, and operational dimensions, including assessment, privacy, transparency, accountability, equity, monitoring, and training.
Why it matters
Makes AI literacy an institutional ecology problem rather than an individual student deficit.
Open source / DOI ↗Study 10
Critical literacy of AI: A scoping review of how children and youth reflect on AI’s wider implications
Veldhuis, A., et al. (2025). International Journal of Child-Computer Interaction, 43, 100708.
Critical AI literacyYouthScoping review
What this adds
Organizes critical AI literacy around disrupting the commonplace, multiple viewpoints, sociopolitical analysis, and taking action. Identifies bias, accountability, surveillance, privacy, transparency, and democratic participation across interventions.
Why it matters
Demonstrates that critical literacy requires more than error detection and should include power, representation, and action.
Open source / DOI ↗Study 11
Critical and creative pedagogies for artificial intelligence and data literacy: An epistemic data justice approach
Atenas, J., Havemann, L., & Nerantzi, C. (2025). Research in Learning Technology, 32, 3296.
Data justiceHigher educationConceptual / synthesis
What this adds
Uses data justice to connect AI and data literacy with visibility, nondiscrimination, participation, privacy, racial and gender justice, decoloniality, sustainability, and community engagement.
Why it matters
Expands AI literacy from individual competence toward democratic participation and epistemic justice.
Open source / DOI ↗Study 12
AI competency framework for students
Miao, F., Shiohira, K., & Lao, N. (2024). UNESCO.
Competency frameworksStudentsInternational framework
What this adds
Combines a human-centred mindset, ethics, AI techniques/applications, and system design across Understand, Apply, and Create.
Why it matters
Provides a widely portable developmental reference that treats human agency and ethics as part of AI competence.
Open source / DOI ↗Study 13
AI competency framework for teachers
Miao, F., & Cukurova, M. (2024). UNESCO.
Faculty developmentTeachers / facultyInternational framework
What this adds
Defines teacher competencies across human-centred mindset, ethics, AI foundations/applications, pedagogy, and professional development.
Why it matters
Provides a developmental structure for faculty support that goes beyond one-off tool workshops.
Open source / DOI ↗Study 14
A framework for the learning and teaching of Critical AI Literacy skills
Hauck, M., et al. (2025). The Open University. Version 0.1.
Critical AI literacyStudents + educatorsInstitutional framework
What this adds
Applies an EDIA lens across AI concepts, teaching, creativity, ethics, society, and careers, with explicit attention to verification, power, labor, environmental impact, accessibility, and epistemic injustice.
Why it matters
Offers one of the strongest bridges between critical theory and concrete higher-education teaching practice.
Open source / DOI ↗Study 15
Critical AI literacy and RACBAC
Zhang, et al. (2025). International Journal of Librarianship, 10(2).
VerificationStudents + researchers + librariansApplied framework
What this adds
Connects data transparency, sovereignty, labor, environmental impact, automation bias, and digital ableism with RACBAC evaluation: Relevance, Accuracy, Coverage, Bias, Authority, and Currency.
Why it matters
Creates a practical bridge between structural critique and day-to-day research verification.
Open source / DOI ↗Study 16
Advancing freshman skills in information literacy and self-regulation: The role of AI learning companions and Mandala chart in academic libraries
Hu, Y.-H., Hsieh, C.-L., & Salac, E. S. N. (2024). The Journal of Academic Librarianship, 50, 102885.
Libraries & learningFirst-year studentsEmpirical
What this adds
Examines an academic-library learning intervention connecting AI-supported learning with information literacy and self-regulation.
Why it matters
Adds intervention evidence to a literature that otherwise contains many perception studies and comparatively fewer evaluated teaching practices.
Open source / DOI ↗