Generative AI often acts yet lacks clear accountability in education
Who Acts, Who Knows, Who Answers? A Corpus-Assisted Discourse Analysis of Agency, Epistemic Responsibility, and Accountability in Generative AI Higher Education Research
Computers and Society
Summary
The study found that in higher education research, generative AI is frequently described as doing actions but is rarely held responsible for outcomes. Instead, responsibility usually falls on educators, institutions, or policies. The authors looked at thousands of research titles and abstracts to see who is seen as acting, knowing, or accountable when AI is involved. This reveals a gap between what AI does and who is actually answerable for its use.
What this means in practice
- •For university policy makers: Clarify roles and responsibilities when adopting AI tools based on insights about who is usually held accountable in educational AI research.
- •For edtech product teams: Design AI systems with clearer user accountability mechanisms informed by common discourse patterns around AI agency and responsibility in education.
Authors
Biranchi Poudyal
Abstract
Generative artificial intelligence (GenAI) is increasingly described in higher education as a tool, collaborator, evaluator, proxy, and infrastructure. These labels are not neutral: they shape who is seen as acting, knowing, and, crucially, being answerable when someone uses AI. This study examines how scholars distribute such responsibility across 366 English-language titles and abstracts published between November 2022 and 14 August 2026. A corpus-assisted discourse analysis combined frequency counts, five-word collocations, actor-predicate associations, obligation-clause coding, and heuristic analysis of passives, nominalisations, and reporting metonymies. The final corpus contained 91,405 tokens, drawn from 8,734 unique records screened after deduplication. AI was the most frequently named actor, appearing 2,050 times and entering 447 predicate associations, most involving action. Students, by contrast, were more often associated with knowing, judging, and verifying, yet no sentence ever made AI explicitly responsible. Responsibility fell largely on educators, institutions, and policy, or disappeared through passive and nominalised constructions. Even frequent phrases such as "responsible AI" and "responsible use" rarely identify who is accountable for what. The findings reveal a persistent gap between giving AI functional agency and assigning normative accountability.