Citation

Shannaq, Boumedyen and AlMaqbali, Said and Ali, Oualid (2026) AI-Transparency and Algorithmic Trust in Smart LMS Adoption: An Explainable-AI Extended TAM Model. In: 2026 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 06-07 May 2026, Manama, Bahrain.

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Abstract

With artificial intelligence (AI) integrated into Learning Management Systems (LMS), there has been academic discussion on problems of transparency, trust, and user anxiety, but these issues have not been properly researched in the conventional Technology Acceptance Model (TAM). This present work responds in this way by adding to the TAM framework with constructs of AI Decision Transparency, Trust in AI Decisions, and positioning AI Anxiety as a moderating variable. The survey was conducted through quantitative methodology, using convenience sampling, a total of 276 valid responses were received and further data analysis was done using SmartPLS. Behavioral Intention was explained by the structural model at 59.5 per cent (R 2 = 0.595). It has been found that AI Decision Transparency has a strong positive correlation with Trust (0.626, p 0.001) and that Trust, in its turn is significantly correlated with Perceived Usefulness (0.432, p 0.001) as well as Behavioral Intention (0.158, p 0.001). Even though the direct effect of AI Anxiety on the intention was substantial (0.321, p 0.001), the hypothesised moderating role was not reinforced. Comprehensively, the results emphasize the fact that transparency-based trust is a key process that enables the adoption of AI-enabled LMS, thus providing credible theoretical evidence and practical suggestions to the ethical use of AI in higher education.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	AI Decision Transparency, Trust in AI Decisions, AI Anxiety, Extended TAM, Learning Management Systems, e-Learning Adoption)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Applied Communication (FAC)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	31 Jul 2026 06:50
Last Modified:	31 Jul 2026 06:50
URII:	http://shdl.mmu.edu.my/id/eprint/16421

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