How is AI Impacting Students?

AI impacts students in both positive and negative directions, and usually at the same time. The same tool can scaffold a student's learning while inviting over-reliance, raise motivation while eroding agency, or support belonging while threatening authorship. Research on the Student Experience points to recurring positive and negative impacts across several dimensions — and the direction depends heavily on how the AI is designed and how students use it.

Positive impacts

1. Support for learning. AI can scaffold understanding with Feedback, hints, and explanations, giving students on-demand help, practice, and adaptive support that keeps the learner doing the cognitively important work. Done well, this supports learning and Help Seeking (see Does using AI actually help students learn?).

2. Motivation and engagement. Personalized, immediate, and low-stakes support can raise Motivation and engagement, helping students persist and feel competent (see self-determination theory and its needs for autonomy, competence, and relatedness).

3. Access and equity in some dimensions. AI can give students who are reluctant to ask questions a low-pressure way to get help, and can support Well Being by reducing anxiety about seeking assistance.

4. Identity and future-readiness. AI can help students build transferable skills for an AI-integrated world — AI Literacy, Distributed Cognition, and Metacognition (see adaptive capabilities) — and support identity formation as students come to see themselves as capable, AI-fluent practitioners.

Negative impacts

1. Over-reliance and the performance–learning gap. AI invites cognitive offloading — students delegate the reasoning they need to practice. This can raise performance with AI while lowering later unassisted performance: the knowledge base's AI misuse and learning harm synthesis documents a field RCT where an unguarded AI tutor improved practice but reduced later exam performance. Overuse can also erode Metacognition and Self Regulated Learning.

2. Reduced effort and agency. Knowing AI is available can reduce students' willingness to struggle productively (AI availability and motivation), and passive acceptance of AI output can erode Agency and the sense of accomplishment that comes from doing work oneself (see the safety gap).

3. Anxiety, shame, and wellbeing costs. AI use is associated with anxiety and stress — about being replaced, uncertain assessment, or keeping up (see comprehensive analysis of AI anxiety). Shame and guilt around AI use can drive hiding and selective disclosure, harming honest engagement and social-emotional wellbeing.

4. Threats to learner identity. When AI produces the work, students may stop feeling the result is "theirs" — an authorship threat to learner identity. The competence paradox in creative fields shows ease-of-use undermining the craft-based identity students derive from authorship.

5. Integrity and equity risks. AI enables new forms of academic dishonesty, and unequal access to (and understanding of) AI tools can widen gaps between students — a core concern of Equity.

The bottom line

The guardrail is to use AI as a scaffold, not a substitute — keep the learner doing the cognitively important work while AI provides support — and to watch the full range of impacts (cognitive, motivational, affective, identity, social, and equity), not just performance. For a deeper treatment organized by dimension, see the Student Experience concept page.