Can Multimodal Large Language Models Enhance Performance Benefits Among Higher Education Students? An Investigation Based on the Task–Technology Fit Theory and the Artificial Intelligence Device Use Acceptance Model

Amany Al-Dokhny (), Omar Alismaiel, Samia Youssif, Nermeen Nasr, Amr Drwish and Amira Samir
Additional contact information
Amany Al-Dokhny: Curriculum and Instruction Department, College of Education, King Faisal University, Al-Ahsa, P.O. Box 400, Hofuf 31982, Saudi Arabia
Omar Alismaiel: Curriculum and Instruction Department, College of Education, King Faisal University, Al-Ahsa, P.O. Box 400, Hofuf 31982, Saudi Arabia
Samia Youssif: Educational Technology Department, College of Specific Education, Ain Shams University, Cairo 11566, Egypt
Nermeen Nasr: Educational Technology Department, College of Specific Education, Ain Shams University, Cairo 11566, Egypt
Amr Drwish: Curriculum and Instruction Department, College of Education, King Faisal University, Al-Ahsa, P.O. Box 400, Hofuf 31982, Saudi Arabia
Amira Samir: Educational Technology Department, College of Specific Education, Ain Shams University, Cairo 11566, Egypt

Sustainability, 2024, vol. 16, issue 23, 1-28

Abstract: The current study highlights the potential of multimodal large language models (MLLMs) to transform higher education by identifying key factors influencing their acceptance and effectiveness. Aligning technology features with educational needs can enhance student engagement and learning outcomes. The study examined the role of MLLMs in enhancing performance benefits among higher education students, using the task–technology fit (T-TF) theory and the artificial intelligence device use acceptance (AIDUA) model. A structured questionnaire was used to assess the perceptions of 550 Saudi university students from various academic disciplines. The data were analyzed via structural equation modeling (SEM) using SmartPLS 3.0. The findings revealed that social influence negatively affected effort expectancy regarding MLLMs and that hedonic motivation was also negatively related to effort expectancy. The findings revealed that social influence and hedonic motivation negatively affected effort expectancy for MLLMs. Effort expectancy was also negatively associated with T-TF in the learning context. In contrast, task and technology characteristics significantly influenced T-TF, which positively impacted both performance benefits and the willingness to accept the use of MLLMs. A strong relationship was found between adoption willingness and improved performance benefits. The findings empower educators to strategically enhance MLLMs adoption strategically, driving transformative learning outcomes.

Keywords: ChatGPT; technology acceptance model (TAM); Unified Theory of Use and Acceptance Technology (UTAUT); cognitive appraisal theory (CAT); generative artificial intelligence (GAI); task–technology fit theory (T-TF) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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