IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10780-d1539744.html

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 Acceptance Model," Sustainability, MDPI, vol. 16(23), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10780-:d:1539744
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10780/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/23/10780/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fang Wang & Tommy Tanu Wijaya & Akhmad Habibi & Yixuan Liu, 2022. "Predictors Influencing Urban and Rural Area students to Use Tablet Computers as Learning Tools: Combination of UTAUT and TTF Models," Sustainability, MDPI, vol. 14(21), pages 1-22, October.
    2. Adam Malešević & Dušan Barać & Dragan Soleša & Ema Aleksić & Marijana Despotović-Zrakić, 2021. "Adopting xRM in Higher Education: E-Services Outside the Classroom," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    3. Fahdah AlShaikh & Rawan Hayan Alwadai, 2025. "A Digital Health Equity Framework for Sustainable e-Health Services in Saudi Arabia," Sustainability, MDPI, vol. 17(17), pages 1-23, August.
    4. Rehman, Anis ur & Behera, Rajat Kumar & Islam, Md. Saiful & Abbasi, Faraz Ahmad & Imtiaz, Asma, 2024. "Assessing the usage of ChatGPT on life satisfaction among higher education students: The moderating role of subjective health," Technology in Society, Elsevier, vol. 78(C).
    5. Tsai, Pei-Hsuan & Tang, Jia-Wei & Ou, Mei-Ling, 2024. "Applying the extended UTAUT-2 to assess the factors contributing to consumers’ usage intention towards over-the-top video streaming platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    6. Mohd Shafie Rosli & Nor Shela Saleh & Azlah Md. Ali & Suaibah Abu Bakar & Lokman Mohd Tahir, 2022. "A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    7. Renjie Song & Yaru Zheng, 2024. "Predicting Continuance Intention to Use Learning Management Systems among Undergraduates: The Moderating Effect of Intrinsic Motivation," SAGE Open, , vol. 14(3), pages 21582440241, August.
    8. Ibrahim A. Elshaer & Sameer M. AlNajdi & Mostafa A. Salem, 2025. "Sustainable AI Solutions for Empowering Visually Impaired Students: The Role of Assistive Technologies in Academic Success," Sustainability, MDPI, vol. 17(12), pages 1-18, June.
    9. Abdulrazaq Kayode Abdulkareem, 2025. "Examining the Influence of Task-Technology Fit in the Interplay of Work Motivation and Job Satisfaction in the Public Sector," Public Organization Review, Springer, vol. 25(2), pages 541-560, June.
    10. Antonio Pérez-Portabella & Mario Arias-Oliva & Graciela Padilla-Castillo & Jorge de Andrés-Sánchez, 2025. "Correlational and Configurational Perspectives on the Determinants of Generative AI Adoption Among Spanish Zoomers and Millennials," Societies, MDPI, vol. 15(10), pages 1-24, October.
    11. Zhou, Min & Huang, Jinlong & Wu, Kexin & Huang, Xin & Kong, Nan & Campy, Kathryn S., 2021. "Characterizing Chinese consumers’ intention to use live e-commerce shopping," Technology in Society, Elsevier, vol. 67(C).
    12. Vaclav Moravec & Beata Gavurova & Nik Hynek & Martin Rigelsky, 2025. "Human-machine in the vortex of digital synergy," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    13. Kalisz, David E., 2025. "Drivers of artificial intelligence integrated healthcare applications. Patients' perspective," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    14. Xiulan Chen & Xiaofei Xu & Yenchun Jim Wu & Wei Fong Pok, 2022. "Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    15. Fan Zhang & Jianbo Pang & Yanlong Guo & Yelin Zhu & Han Zhang, 2023. "A Study of Factors Influencing the Use of the DingTalk Online Lecture Platform in the Context of the COVID-19 Pandemic," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    16. Bassel El Hajj & Georgiana Karadas & Pouya Zargar, 2023. "How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    17. Cecilia Temilola Olugbara & Moeketsi Letseka & Oludayo O. Olugbara, 2021. "Multiple Correspondence Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses," Sustainability, MDPI, vol. 13(23), pages 1-21, December.
    18. Shujie Wang & Mohammed Habes Ibrahiem & Mengyu Li, 2022. "Motivations Influencing Alipay Users to Participate in the Ant Forest Campaign: An Empirical Study," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    19. repec:bcp:journl:v:9:y:2025:i:10:p:3262-3271 is not listed on IDEAS
    20. Zhonggen Yu & Liheng Yu, 2023. "Examining Factors That Influence Learner Retention in MOOCs During the COVID-19 Pandemic Time," SAGE Open, , vol. 13(2), pages 21582440231, May.
    21. Dang, Simon & Quach, Sara & Roberts, Robin E., 2025. "How time fuels AI device adoption: A contextual model enriched by machine learning," Technological Forecasting and Social Change, Elsevier, vol. 212(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10780-:d:1539744. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.