Theorizing the Impact of AI Tools Usage on Students' Motivation in Technical Higher Education: A Multidimensional Conceptual Framework

Abstract

The fast use of AI (artificial intelligence) systems in higher education has changed the way students access material, get feedback, do academic assignments, and manage their learning. Despite the widespread implementation of AI, the motivational effects of using AI tools remain underexplored, especially in technical higher education contexts, where applied learning, digital competence, and Education 4.0 requirements are major institutional concerns. This conceptual paper suggests a multidimensional theoretical framework to study the influence of AI tools usage on student motivation among first-year undergraduate students in the Malaysian Technical University Network (MTUN), which consists of Universiti Malaysia Perlis (UniMAP), Universiti Tun Hussein Onn Malaysia (UTHM), Universiti Teknikal Malaysia Melaka (UTeM), and Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA). The framework is a focused, theoretically bounded integration of the Technology
Acceptance Model (TAM) and Self-Determination Theory (SDT). The operationalization of AI technology use is based on three dimensions: frequency of use, purpose of use, and perceived
usefulness. Student motivation is operationalized by three SDT-based dimensions: intrinsic motivation, extrinsic motivation, and amotivationa unique negative result of motivation characterized by the lack of deliberate academic engagement. Nine formal propositions specify the directional relationships among these dimensions, with a central argument that AI tools’ motivational impact is conditional: AI tools may support intrinsic and extrinsic motivation when used deliberately for learning-oriented purposes, but may increase amotivation when used excessively, in a dependency-driven manner, or as shortcuts that bypass genuine cognitive effort. This paper presents a parsimonious integration of TAM and SDT that incorporates motivational quality, the treatment of amotivation as a distinct theoretical
outcome, the specification of conditional directionality in AI-motivation relationships, and contextual grounding in MTUN's technical education environment. A recommended methodology for future empirical validation using PLS-SEM is discussed, and practical implications are provided for MTUN policy makers, lecturers, curriculum designers, and students.