Skip to main content
  • Chen, X., Hu, Z. & Wang, C. Empowering education development through AIGC: A systematic literature review. Educ. Inform. Technol. 29 (13), 17485–17537 (2024).

    Article 

    Google Scholar
     

  • Mogavi, R. H. et al. ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers Hum. Behavior: Artif. Hum. 2 (1), 100027 (2024).

    Article 

    Google Scholar
     

  • Ghnemat, R., Shaout, A. & Abrar, M. Higher education transformation for artificial intelligence revolution: transformation framework. Int. J. Emerg. Technol. Learn. 17 (19), 224–241 (2022).

    Article 

    Google Scholar
     

  • Santos, A. I. & Serpa, S. Artificial intelligence and higher education. Int. Soc. Technol. Educ. Sci. 1, 1 (2023).


    Google Scholar
     

  • Huraj, L., Pospíchal, J. & Luptáková, I. D. Learning enhancement with AI: From idea to implementation. In 2023 21st International Conference on Emerging eLearning Technologies and Applications (ICETA) 212–219 (IEEE, 2023).

  • Gallegos, M. D. C. J., Chisag, W. D. A., Valencia, D. A. Z. & Saltos, N. E. C. Impacto de La inteligencia artificial En La educación superior: percepciones de alumnos y profesores sobre El Uso de IA En El Aprendizaje y La evaluación. Reincisol 3 (6), 7008–7033 (2024).

    Article 

    Google Scholar
     

  • Kuka, L., Hörmann, C. & Sabitzer, B. Teaching and learning with AI in higher education: A scoping review. In Learning with Technologies and Technologies in Learning: Experience, Trends and Challenges in Higher Education 551–571 (2022).

  • WIPO. Artificial Intelligence. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf (2019).

  • UNESCO. ChatGPT and Artificial Intelligence in Higher Education. https://www.iesalc.unesco.org/wp-content/uploads/2023/04/ChatGPT-and-Artificial-Intelligence-in-higher-education-Quick-Start-guide_EN_FINAL.pdf (2023).

  • Pelletier, K. et al. 2024 Educause Horizon Report: Teaching and Learning Edition. https://library.educause.edu/resources/2024/5/2024-educause-horizon-report-teaching-and-learning-edition (Accessed 12 November 2024) (2024).

  • Cai, Q., Lin, Y. & Yu, Z. Factors influencing learner attitudes towards ChatGPT-assisted Language learning in higher education. Int. J. Hum. Comput. Interact. 40 (22), 7112–7126 (2024).

    Article 

    Google Scholar
     

  • Jing, Y., Wang, H., Chen, X. & Wang, C. What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit. Social Sci. Commun. 11 (1), 1–12 (2024).

    Article 

    Google Scholar
     

  • Chen, X., Zou, D., Xie, H. & Wang, F. L. Past, present, and future of smart learning: a topic-based bibliometric analysis. Int. J. Educational Technol. High. Educ. 18 (1), 2 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Hwang, G. J., Tu, Y. F. & Lin, C. J. Advancements and hot research topics of artificial intelligence in mobile learning: A review of journal publications from 1995 to 2019. Int. J. Mob. Learn. Organisation. 15 (4), 427–447 (2021).

    Article 

    Google Scholar
     

  • Tang, K. Y., Chang, C. Y. & Hwang, G. J. Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interact. Learn. Environ. 31 (4), 2134–2152 (2023).

    Article 

    Google Scholar
     

  • Bond, M. et al. A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. Int. J. Educational Technol. High. Educ. 21 (1), 4 (2024).

    Article 

    Google Scholar
     

  • Alajmi, Q., Al-Sharafi, M. A. & Abuali, A. Smart learning gateways for Omani HEIs towards educational technology: benefits, challenges and solutions. Int. J. Inform. Technol. Lang. Stud. 4 (1), 12–17 (2020).


    Google Scholar
     

  • Wang, C., Chen, X., Yu, T., Liu, Y. & Jing, Y. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit. Social Sci. Commun. 11 (1), 1–17 (2024).


    Google Scholar
     

  • Mahmoud, M. M. H. & Othman, R. Performance management system in developing countries: A case study in Jordan. J. Public. Affairs 23 (4), e2864 (2023).

  • Sharma, H., Soetan, T., Farinloye, T., Mogaji, E. & Noite, M. D. F. AI adoption in universities in emerging economies: prospects, challenges and recommendations. In Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises 159–174 (Springer, 2022).

  • Rodzi, Z. M. et al. Unraveling the drivers of artificial intelligence (AI) adoption in higher education. In. 2023 International Conference on University Teaching and Learning (InCULT) 1–6 (IEEE, 2023).

  • Velastegui, D., Pérez, M. L. R. & Garcés, L. F. S. Impact of artificial intelligence on learning behaviors and psychological well-being of college students. Salud Ciencia Y Tecnologia-Serie De Conferencias. 2, 343 (2023).


    Google Scholar
     

  • Rodzi, Z. M. et al. Unraveling the factors influencing the adoption of artificial intelligence (AI) in education. In. 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS) 186–193 (IEEE, 2023).

  • Aladi, C. C. IT higher education teachers and trust in AI-enabled Ed-Tech: Implications for adoption of AI in higher education. In Proceedings of the 2024 Computers and People Research Conference 1–16 (2024).

  • Widyaningrum, R., Wulandari, F., Zainudin, M., Athiyallah, A. & Rizqa, M. Exploring the factors affecting ChatGPT acceptance among university students. Multidisciplinary Sci. J. 6 (12), 2024273 (2024).

    Article 

    Google Scholar
     

  • Morales-García, W. C. et al. Development and validation of a scale for dependence on artificial intelligence in university students. Front. Educ. 9, 1323898 (2024).

    Article 

    Google Scholar
     

  • Estrada-Araoz, E. G. et al. Assessment of the level of knowledge on artificial intelligence in a sample of university professors: a descriptive study. Data Metadata. 3, 285 (2024).

    Article 

    Google Scholar
     

  • Chai, C. S., Yu, D., King, R. B. & Zhou, Y. Development and validation of the artificial intelligence learning intention scale (AILIS) for university students. Sage Open. 14 (2), 21582440241242188 (2024).

    Article 

    Google Scholar
     

  • Kasneci, E. et al. ChatGPT for good? On opportunities and challenges of large Language models for education. Learn. Individual Differences. 103, 102274 (2023).

    Article 

    Google Scholar
     

  • Dai, W. et al. Assessing the proficiency of large Language models in automatic feedback generation: an evaluation study. Computers Education: Artif. Intell. 7, 100299 (2024).


    Google Scholar
     

  • Kuhail, M. A., Alturki, N., Alramlawi, S. & Alhejori, K. Interacting with educational chatbots: A systematic review. Educ. Inform. Technol. 28 (1), 973–1018 (2023).

    Article 

    Google Scholar
     

  • Wambsganss, T., Kueng, T., Soellner, M. & Leimeister, J. M. ArgueTutor: An adaptive dialog-based learning system for argumentation skills. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 1–13 (2021).

  • Ahuja, A. S., Polascik, B. W., Doddapaneni, D., Byrnes, E. S. & Sridhar, J. The digital metaverse: applications in artificial intelligence, medical education, and integrative health. Integr. Med. Res. 12 (1), 100917 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Frej, J., Shah, N., Knezevic, M., Nazaretsky, T. & Käser, T. Finding paths for explainable mooc recommendation: A learner perspective. In Proceedings of the 14th Learning Analytics and Knowledge Conference 426–437 (2024).

  • Celik, I., Dindar, M., Muukkonen, H. & Järvelä, S. The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends 66 (4), 616–630 (2022).

    Article 

    Google Scholar
     

  • Chrisinger, D. The solution Lies in education: artificial intelligence & the skills gap. Horizon 27 (1), 1–4 (2019).

    Article 

    Google Scholar
     

  • Ahmad, T. Scenario based approach to re-imagining future of higher education which prepares students for the future of work. High. Educ. Skills Work-Based Learn. 10 (1), 217–238 (2020).

    Article 

    Google Scholar
     

  • Kelly, S., Kaye, S. A. & Oviedo-Trespalacios, O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics Inform. 77, 101925 (2023).

    Article 

    Google Scholar
     

  • Yan, L. et al. Practical and ethical challenges of large Language models in education: A systematic scoping review. Br. J. Edu. Technol. 55 (1), 90–112 (2024).

    Article 

    Google Scholar
     

  • Smith, P. J. Learning preferences and readiness for online learning. Educational Psychol. 25 (1), 3–12 (2005).

    Article 
    MathSciNet 

    Google Scholar
     

  • Tang, Y. M. et al. Comparative analysis of student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Comput. Educ. 168, 104211 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dai, Y. et al. Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability 12 (16), 6597 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: toward a unified view. MIS Quarterly, 425–478. (2003).

  • Sudaryanto, M., Hendrawan, R., A., M. & Andrian, T. The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption. E3S Web Conferences. 388, p04055 (2023).

    Article 

    Google Scholar
     

  • Falcone, R. & Castelfranchi, C. Social trust: A cognitive approach. Trust and Deception in Virtual Societies 55–90 (2001).

  • Kesharwani, A. & Singh Bisht, S. The impact of trust and perceived risk on internet banking adoption in india: an extension of technology acceptance model. Int. J. Bank. Mark. 30 (4), 303–322 (2012).

    Article 

    Google Scholar
     

  • Kim, J. & Gambino, A. Do we trust the crowd or information system? Effects of personalization and bandwagon cues on users’ attitudes and behavioral intentions toward a restaurant recommendation website. Comput. Hum. Behav. 65, 369–379 (2016).

    Article 

    Google Scholar
     

  • Bikanga Ada, M. It helps with crap lecturers and their low effort: investigating computer science students’ perceptions of using Chatgpt for learning. Educ. Sci. 14 (10), 1106 (2024).

    Article 

    Google Scholar
     

  • Bubaš, G., Čižmešija, A. & Kovačić, A. Development of an assessment scale for measurement of usability and user experience characteristics of Bing chat conversational AI. Future Internet. 16 (1), 4 (2023).

    Article 

    Google Scholar
     

  • Kamoun, F., El Ayeb, W., Jabri, I., Sifi, S. & Iqbal, F. Exploring students’ and faculty’s knowledge, attitudes, and perceptions towards chatgpt: a cross-sectional empirical study. J. Inform. Technol. Education: Res. 23, 1 (2024).


    Google Scholar
     

  • Ng, D. T. K. et al. A review of AI teaching and learning from 2000 to 2020. Educ. Inform. Technol. 28 (7), 8445–8501 (2023).

    Article 

    Google Scholar
     

  • Baidoo-Anu, D. & Ansah, L. O. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. J. AI. 7 (1), 52–62 (2023).

    Article 

    Google Scholar
     

  • De la Vall, R. R. F. & Araya, F. G. Exploring the benefits and challenges of AI-language learning tools. Int. J. Social Sci. Humanit. Invention. 10 (01), 7569–7576 (2023).

    Article 

    Google Scholar
     

  • Cisneros, J. D. D. et al. Adjustment of Peruvian university students to artificial intelligence. Arts Educ. 36, 1 (2023).


    Google Scholar
     

  • Zhu, W. et al. Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university students’ use of generative AI products? An ethical perspective. Int. J. Hum. Comput. Interact. 41 (1), 742–764 (2025).

    Article 

    Google Scholar
     

  • Creswell, J. W. & Creswell, J. D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (Sage Publications, 2017).

  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. Multivariate Data Analysis (2019).

  • Sanusi, I. T., Ayanwale, M. A. & Chiu, T. K. Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Educ. Inform. Technol. 29 (1), 273–295 (2024).

    Article 

    Google Scholar
     

  • Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J. & Käser, T. The critical role of trust in adopting AI-powered educational technology for learning: an instrument for measuring student perceptions. Computers Education: Artif. Intell. 1, 100368 (2025).


    Google Scholar
     

  • Yaseen, H. et al. The impact of adaptive learning technologies, personalized feedback, and interactive AI tools on student engagement: the moderating role of digital literacy. Sustainability 17 (3), 1133 (2025).

    Article 
    ADS 

    Google Scholar
     

  • Rahman, M. S., Sabbir, M. M., Zhang, J., Moral, I. H. & Hossain, G. M. S. Examining students’ intention to use chatgpt: does trust matter? Australasian J. Educational Technol. 39 (6), 51–71 (2023).

    Article 

    Google Scholar
     

  • Acosta-Enriquez, B. G. et al. What is the influence of psychosocial factors on artificial intelligence appropriation in college students? BMC Psychol. 13 (1), 7 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kline, R. B. Principles and Practice of Structural Equation Modeling (Guilford Publications, 2023).

  • Byrne, B. M. Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming (Routledge, 2013).

  • Awang, P. SEM Made Simple: A Gentle Approach To Learning Structural Equation Modeling (MPWS Rich Publication, 2015).

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. Multivariate Data Analysis (Prentice Hall, 2010).

  • Cohen, J. Statistical Power for the Behavioural Sciences, vol. 58, 7–19 (Lawrence Erlbaum, 1988).

  • Shirahada, K., Ho, B. Q. & Wilson, A. Online public services usage and the elderly: assessing determinants of technology readiness in Japan and the UK. Technol. Soc. 58, 101115 (2019).

    Article 

    Google Scholar
     

  • Blut, M. & Wang, C. Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. J. Acad. Mark. Sci. 48, 649–669 (2020).

    Article 

    Google Scholar
     

  • Cyr, D., Head, M. & Ivanov, A. Perceived interactivity leading to e-loyalty: development of a model for cognitive–affective user responses. Int. J. Hum. Comput. Stud. 67 (10), 850–869 (2009).

    Article 

    Google Scholar
     

  • Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L. & Ogundipe, D. O. Revolutionizing education through AI: A comprehensive review of enhancing learning experiences. Int. J. Appl. Res. Social Sci. 6 (4), 589–607 (2024).

    Article 

    Google Scholar
     

  • Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O. & Bassey, B. A. Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia J. Math. Sci. Technol. Educ. 19 (8), 2307 (2023).

    Article 

    Google Scholar
     

  • Chen, X. & Ibrahim, Z. A comprehensive study of emotional responses in ai-enhanced interactive installation Art. Sustainability 15 (22), 15830 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Alshammari, S. H., Almankory, A. Z. & Alrashidi, M. E. The effects of awareness and trust on students’ willingness to use chatgpt: an integrated TAM-ECM model. Rev. Iberoam. Educ. Dist. 28 (2), 1 .

  • Cheng, I. H. & Lee, S. T. The impact of ethics instruction and internship on students’ ethical perceptions about social media, artificial intelligence, and ChatGPT. J. Media Ethics. 39 (2), 114–129 (2024).

    Article 

    Google Scholar
     

  • Kong, S. C., Cheung, W. M. Y. & Zhang, G. Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technol. Soc. 26 (1), 16–30 (2023).


    Google Scholar
     

  • Awal, M. R. & Haque, M. E. Revisiting university students’ intention to accept AI-powered chatbot with an integration between TAM and SCT: a South Asian perspective. J. Appl. Res. High. Educ. 17 (2), 594–608 (2025).

    Article 

    Google Scholar
     

  • Bouteraa, M. et al. Understanding the diffusion of AI-generative (ChatGPT) in higher education: does students’ integrity matter? Computers Hum. Behav. Rep. 14, 100402 (2024).

    Article 

    Google Scholar
     

  • Chocarro, R., Cortiñas, M. & Marcos-Matás, G. Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Stud. 49 (2), 295–313 (2023).

    Article 

    Google Scholar
     

  • Du, L. & Lv, B. Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Educ. Inform. Technol. 1, 1–20 (2024).


    Google Scholar
     

  • Pusposari, D., Rachman, O. A. & Kusumadewi, A. W. The determinants of students’ intentions to use artificial intelligence-based mobile investment apps. Int. J. Acc. Bus. Soc. 32 (3), 249–256 (2024).


    Google Scholar
     

  • Source link

    Subscribe our Newsletter

    Congratulation!