Human-Centered Generative AI in Education: Assessment, Agency, and Equity
The widespread availability of Generative Artificial Intelligence (GenAI) is reshaping learning, teaching, and assessment across educational contexts. GenAI can function as a tutor, writing partner, dialogue practice tool, or co-designer of learning activities. At the same time, its use challenges established practices related to assessment, feedback, authorship, and accountability, raising fundamental questions about human agency in educational decision-making.
Education systems face a dual challenge: integrating GenAI in ways that enhance teaching and learning while safeguarding academic integrity, equity, and professional responsibility. Students must retain the capacity to set goals, make informed choices, and justify their work, while teachers need to exercise professional judgement to determine when and how GenAI meaningfully supports learning, and when it may undermine pedagogical intent. Agency, in this sense, is not only an individual attribute but is shaped by social relationships, classroom norms, institutional policies, and governance structures that enable or constrain practice.
This Research Topic aims to advance empirical evidence and practical frameworks for human-centered approaches to GenAI in education, treating AI not as a one-size-fits-all solution but as a tool embedded within social, cultural, and organizational contexts. We seek contributions that examine when and how GenAI supports learning and teaching, what risks may emerge – such as overreliance, misinformation, bias, privacy concerns, and opacity – and which instructional, professional, and institutional strategies can mitigate these risks.
We are particularly interested in research that explores how GenAI reconfigures student agency and teacher professional judgement in assessment, feedback, and everyday classroom decision-making, including how responsibility and accountability are allocated when AI systems are used. Relevant contexts include K–12 education, higher education, and teacher education or professional learning settings.
We invite Original Research, Reviews, Systematic Reviews, Perspectives, and Methods papers addressing, but not limited to:
– Assessment redesign and authentic tasks in the presence of AI-generated content
– Feedback and formative assessment supported by GenAI
– Academic integrity, transparency, and responsible disclosure of AI use
– AI literacy integrating critical evaluation, metacognition, and ethical reasoning
– Equity, accessibility, disability inclusion, and the digital divide
– Institutional policies, governance, procurement, and implementation of GenAI
– Discipline-specific studies evaluating learning outcomes in AI-supported contexts
By foregrounding human agency and professional judgement, this Research Topic seeks to support educators, institutions, and policymakers in adopting GenAI in ways that are pedagogically sound, ethically responsible, and socially equitable.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Data Report
- Editorial
- FAIR² Data
- FAIR² DATA Direct Submission
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
- … View all formats
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: governance, generative AI, assessment, academic integrity, teacher agency, student agency, teacher professional judgement, AI literacy, equity
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
