Psychological Predictors of AI-Assisted Learning Adoption among University Students in India and Uzbekistan: A Conceptual Framework of Psychological AI Readiness
DOI: 10.23951/2782-2575-2026-3-34-56
Introduction. The rapid proliferation of artificial intelligence (AI) tools in higher education has opened new frontiers in pedagogical delivery and student engagement. Yet the pace of AI adoption among university students remains uneven and is shaped more by psychological than purely technological factors. Aim and objectives. This paper aims to identify the key psychological predictors of AI-assisted learning adoption among university students in India and Uzbekistan and to develop a conceptual framework that translates these predictors into a practical instrument for educational design. The objectives are to synthesise the relevant theory, compare the two national contexts analytically, operationalise a four-domain model of Psychological AI Readiness (PAR), and outline how the model can be implemented and tested. Materials and methods. The study is a conceptual (theoretical) paper. It employs a structured narrative review of the international and regional literature. It integrates three established theoretical frameworks – the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Social Cognitive Theory (SCT) – to derive and operationalise the proposed framework. No primary empirical data were collected; instead, a pilot study design is proposed for subsequent validation. Results. Five psychological predictors are identified as central to AI adoption: self-efficacy, technology anxiety, intrinsic motivation, perceived usefulness, and social influence. These are organised into the four interactive domains of the PAR framework – cognitive, affective, motivational, and social – each operationalised in terms of what it diagnoses, its measurable indicators, the student difficulties it reveals, the corresponding pedagogical actions, and the training formats that develop it. A comparative analysis of India and Uzbekistan, summarised in a comparative table, shows how the salience of each predictor differs across the two contexts. A structure for an AI Readiness Training Programme and a proposed pilot implementation plan are also presented. Conclusion. The PAR framework offers a theoretically grounded and practically applicable tool for designing psychologically informed AI integration in higher education. It yields concrete benefits for students and teachers and provides a testable basis for future empirical research in India, Uzbekistan, and comparable contexts.
Keywords: artificial intelligence in education, technology adoption, psychological predictors, psychological AI readiness, self-efficacy, technology anxiety, intrinsic motivation, perceived usefulness, social influence, higher education, India, Uzbekistan
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Issue: 3, 2026
Series of issue: Issue 3
Rubric: PEDAGOGY
Pages: 34 — 56
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