A helping hand or a dominant partner? Individual perceptions of
                    <scp>GenAI</scp>
                    reliance and human agency in collaborative learning

doi:10.1111/bjet.70090

DOI: 10.1111/bjet.70090 ISSN: 0007-1013

A helping hand or a dominant partner? Individual perceptions of GenAI reliance and human agency in collaborative learning

Yanyi Wu, Xinyu Lu

Abstract

Generative artificial intelligence (GenAI) has become a routine source of feedback, explanation, drafting support and task guidance in higher education. This study examined how individual students' self‐reported GenAI reliance and perceived human agency co‐varied over time in an AI‐supported collaborative writing course. Three‐wave data were collected from 342 undergraduates working in 85 fixed groups. A traditional cross‐lagged panel model (CLPM) and a random intercept cross‐lagged panel model (RI‐CLPM) were estimated to distinguish observed‐level associations from within‐person temporal relations after stable between‐person differences were separated. The CLPM indicated reciprocal observed‐level associations. In the RI‐CLPM, higher‐than‐usual reliance was associated with lower subsequent agency, whereas agency‐to‐reliance paths were weaker and not statistically supported. Equality‐constraint tests were consistent with directional asymmetry, while 95% confidence intervals and Monte Carlo sensitivity analyses showed that small reverse effects remain possible. Backend logs showed that self‐reported reliance corresponded to AI‐use intensity, although log indicators could not distinguish strategic consultation from cognitive offloading or deference. The findings highlight the importance of designing AI‐supported collaboration so that students' responsibility for interpretation, judgement and authorship remains visible.

Practitioner notes

What is already known about this topic

GenAI systems can provide timely feedback, explanation and drafting support in collaborative learning environments.

AI support may help students generate ideas and revise work, but reliance on system‐generated suggestions can reduce opportunities for judgement, negotiation and ownership.

Traditional cross‐lagged models can conflate stable between‐person differences with within‐person temporal associations.

What this paper adds

The study examines individual students' self‐reported GenAI reliance and perceived human agency across three waves in an AI‐supported collaborative writing course.

After stable between‐person differences were separated, RI‐CLPM estimates provided stronger evidence for paths from higher‐than‐usual reliance to lower subsequent agency than for the reverse direction.

Equality‐constraint tests were consistent with directional pattern asymmetry, while confidence intervals and Monte Carlo sensitivity analyses indicated that small agency‐to‐reliance effects remain possible.

Backend logs showed that self‐reported reliance corresponded to AI‐use intensity, but log indicators could not distinguish strategic consultation from cognitive offloading or deference.

Implications for practice and/or policy

AI‐supported collaborative tasks should make students' responsibility for interpretation, judgement and authorship visible throughout the learning process.

Instructors can ask groups to form an initial position before invoking AI and to explain why AI suggestions are accepted, revised or rejected.

GenAI tools may better support agency by offering critics, asking questions and prompting reflection while leaving decisions about task direction to students.

Learning analytics dashboards should not equate high AI‐use intensity with over‐reliance unless log data are interpreted alongside discourse, reflection, revision or decision‐making evidence.