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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Amany Al-Dokhny & Omar Alismaiel & Samia Youssif & Nermeen Nasr & Amr Drwish & Amira Samir, 2024. "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 A," Sustainability, MDPI, vol. 16(23), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10780-:d:1539744
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    References listed on IDEAS

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    1. Al-Azawei, Ahmed & Alowayr, Ali, 2020. "Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries," Technology in Society, Elsevier, vol. 62(C).
    2. Ibrahim Youssef Alyoussef, 2021. "Massive Open Online Course (MOOCs) Acceptance: The Role of Task-Technology Fit (TTF) for Higher Education Sustainability," Sustainability, MDPI, vol. 13(13), pages 1-14, July.
    3. Jiangjie Chen & Ziqing Zhuo & Jiacheng Lin, 2023. "Does ChatGPT Play a Double-Edged Sword Role in the Field of Higher Education? An In-Depth Exploration of the Factors Affecting Student Performance," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    4. Maricar M. Navarro & Yogi Tri Prasetyo & Michael Nayat Young & Reny Nadlifatin & Anak Agung Ngurah Perwira Redi, 2021. "The Perceived Satisfaction in Utilizing Learning Management System among Engineering Students during the COVID-19 Pandemic: Integrating Task Technology Fit and Extended Technology Acceptance Model," Sustainability, MDPI, vol. 13(19), pages 1-18, September.
    5. Camilleri, Mark Anthony, 2024. "Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework," Technological Forecasting and Social Change, Elsevier, vol. 201(C).
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