Abstract:

Generative Artificial Intelligence is becoming an ordinary component of students’ academic environment in higher education. Yet its educational significance cannot be reduced to gains in speed, convenience, or productivity. The central issue is not only what these systems allow students to produce, but how they reshape students’ engagement with academic tasks, their regulation of effort, their critical judgment, and their relation to knowledge itself. This paper examines students’ uses and perceived effects of Generative Artificial Intelligence through a qualitative, social-conative perspective centred on agency, self-regulation, perceived competence, and meaningful academic engagement.

The study is based on semi-structured interviews conducted with students from different disciplines and academic levels in a French higher education institution. The analysis sought to identify recurrent patterns of use, perceived benefits, experienced tensions, and forms of regulation associated with Generative Artificial Intelligence in academic activity. The findings show that students do not appropriate these tools in a homogeneous way. Their uses vary according to disciplinary context, academic trajectory, digital literacy, and capacity to critically assess generated outputs.

For some students, Generative Artificial Intelligence facilitates entry into the task, reduces uncertainty, supports comprehension, and strengthens the feeling of being able to cope with academic demands. For others, or in other situations, it may encourage cognitive offloading, superficial processing, weaker memorisation, and blurred authorship boundaries when generated content is used without sufficient verification, reformulation, or conceptual appropriation. The study therefore suggests that the educational effects of Generative Artificial Intelligence depend less on the tool itself than on the quality of students’ reflective regulation and on the pedagogical conditions in which use is embedded.

This paper proposes a qualitatively grounded interpretive framework for understanding students’ academic engagement in Generative Artificial Intelligence environments. It argues that sustainable learning with Generative Artificial Intelligence requires more than access to powerful tools. It requires pedagogical designs and assessment practices that support critical thinking, reflective self-regulation, and forms of student activity that remain intellectually demanding, meaningful, and genuinely formative.

Keywords:

Generative Artificial Intelligence, Higher Education, Students’ Academic Engagement, Self-Regulation, Critical Thinking, Qualitative Research.