Artificial Intelligence in Higher Education: Psychological Effects on University Teaching

Aim. To identify GenAI‑specific psychological mechanisms through which LLMs reshape academic teaching work, and to compare effects in content creation versus assessment.

Methodology. The study combines a transparent integrative review (2015–2025; Scopus, WoS, PsycINFO, ERIC, RSCI; 56 sources with PRISMA‑style tracking) with a qualitative pilot (16 semi‑structured interviews with Russian university instructors; thematic analysis) using an integrated JD‑R and SDT framework.

Results: GenAI acts both as a resource and a work‑redesign driver, generating AI‑specific demands: epistemic uncertainty, verification workload, trust calibration, accountability / responsibility anxiety, norm acceleration and “silent” intensification, and a role shift toward surveillance in assessment. A domain asymmetry is found – content work is mainly burdened by hidden cognitive verification, while assessment amplifies emotional and normative risks related to fairness and conflict.

Research implications: a model linking GenAI properties to job demands / resources and basic needs (autonomy, competence, relatedness) is proposed; recommendations include formal time for verification, a human‑in‑the‑loop standard for high‑stakes assessment, clearer academic integrity policies, and safeguards against intensification.

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