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Research Article

Acceptance of Educational Use of AI Chatbots in the Context of Self-Directed Learning with Technology and ICT Self-Efficacy of Undergraduate Students

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Received 28 Aug 2023, Accepted 03 Jan 2024, Published online: 22 Jan 2024

References

  • Ait Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H. (2023). The impact of educational chatbot on student learning experience. Education and Information Technologies, 1–24. https://doi.org/10.1007/s10639-023-12166-w
  • Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education, 4(8), 100132. https://doi.org/10.1016/j.caeai.2023.100132
  • Al-Rahmi, W. M., Alzahrani, A. I., Yahaya, N., Alalwan, N., & Kamin, Y. B. (2020). Digital communication: Information and communication technology (ICT) usage for education sustainability. Sustainability, 12(12), 5052. https://doi.org/10.3390/su12125052
  • Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 31(10), 7491–7510. https://doi.org/10.1080/10494820.2022.2075014
  • Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-covid era and its impact on faculty members’ occupational well-being and teaching self efficacy: A path analysis using the UTAUT 2 model. Applied Artificial Intelligence, 37(1), 2175110. https://doi.org/10.1080/08839514.2023.2175110
  • Almahri, F. A. J., Bell, D., & Merhi, M. (2020). Understanding student acceptance and use of chatbots in the United Kingdom universities: A structural equation modelling approach. In 2020 6th International Conference on Information Management (ICIM) (pp. 284–288). IEEE. https://doi.org/10.1109/ICIM49319.2020.244712
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • BBC. (2023). OpenAI announces ChatGPT successor GPT-4. BBC News. https://www.bbc.com/news/technology-64959346
  • Bizzo, E. (2022). Acceptance and resistance to e-learning adoption in developing countries: A literature review. Ensaio: Avaliação e Políticas Públicas em Educação, 30(115), 458–483. https://doi.org/10.1590/s0104-403620220003003342
  • Bonsu, E., & Baffour-Koduah, D. (2023). From the consumers’ side: Determining students’ perception and intention to use chatGPT in Ghanaian higher education. Journal of Education, Society & Multiculturalism, 4(1), 1–29.
  • Bulla, C., Parushetti, C., Teli, A., Aski, S., & Koppad, S. (2020). A review of AI based medical assistant chatbot. Res Appl Web Dev Des, 3(2), 1–14. https://doi.org/10.5281/zenodo.3902215
  • Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students’ learning achievement and self‐efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171–188. https://doi.org/10.1111/bjet.13158
  • Chen, X., & Hu, J. (2020). ICT-related behavioral factors mediate the relationship between adolescents’ ICT interest and their ICT self-efficacy: Evidence from 30 countries. Computers & Education, 159, 104004. https://doi.org/10.1016/j.compedu.2020.104004
  • Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295–313. https://doi.org/10.1080/03055698.2020.1850426
  • Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and Information Technologies, 27(6), 8723–8750. https://doi.org/10.1007/s10639-021-10866-9
  • Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on adoption and use of ChatGPT: A survey analysis. Journal of Medical Internet Research, 25, e47184. https://doi.org/10/03/2023:47184
  • Cohen, J. (1992). Quantitative methods in psychology. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
  • Demir, Ö., & Yurdugül, H. (2013). Self-directed learning with technology scale for young students: A validation study. E-Journal of International Education Research, 4(3), 58–73.
  • Dharani, M., Jyostna, J. V. S. L., Sucharitha, E., Likitha, R., & Manne, S. (2020). Interactive transport enquiry with ai chatbot. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1271–1276). IEEE. https://doi.org/10.1109/ICICCS48265.2020.9120905
  • Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
  • Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age. Springer International Publishing. https://doi.org/10.1007/978-3-319-14222-7
  • Gokcearslan, S. (2017). Perspectives of students on acceptance of tablets and self-directed learning with technology. Contemporary Educational Technology, 8(1), 40–55. https://doi.org/10.30935/cedtech/6186
  • Gökçearslan, Ş., Yildiz Durak, H., & Atman Uslu, N. (2022). Acceptance of educational use of the Internet of Things (IoT) in the context of individual innovativeness and ICT competency of pre-service teachers. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2022.2091612
  • Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  • Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modelling (PLS-SEM) (3rd ed.) Sage.
  • Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology, 15(2), ep421. https://doi.org/10.30935/cedtech/13036
  • Hamdoun, S., Monteleone, R., Bookman, T., & Michael, K. (2023). AI-based and digital mental health apps: Balancing need and risk. IEEE Technology and Society Magazine, 42(1), 25–36. https://doi.org/10.1109/MTS.2023.3241309
  • Hasan, B. (2006). Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Information & Management, 43(5), 565–571. https://doi.org/10.1016/j.im.2005.11.005
  • Hatlevik, O. E., Throndsen, I., Loi, M., & Gudmundsdottir, G. B. (2018). Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education, 118, 107–119. https://doi.org/10.1016/j.compedu.2017.11.011
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Hong, J. (2022). I was born to love AI: The influence of social status on ai self-efficacy and intentions to use AI. International Journal of Communication, 16, 20. https://ijoc.org/index.php/ijoc/article/view/17728
  • Hong, J. C., Hwang, M. Y., Tai, K. H., & Chen, Y. L. (2014). Using calibration to enhance students’ self-confidence in English vocabulary learning relevant to their judgment of over-confidence and predicted by smartphone self-efficacy and English learning anxiety. Computers & Education, 72, 313–322. https://doi.org/10.1016/j.compedu.2013.11.011
  • Ionescu-Feleaga, L., Ionescu, B. Ș., & Bunea, M. (2021). The IoT technologies acceptance in education by the students from the economic studies in Romania. www.amfiteatrueconomic.ro, 23(57), 342–359. https://doi.org/10.24818/EA/2021/57/342
  • Jiang, M. Y. c., Jong, M. S. y., Lau, W. W. f., Meng, Y. L., Chai, C. S., & Chen, M. (2021). Validating the general extended technology acceptance model for e-learning: Evidence from an online English as a foreign language course amid COVID-19. Frontiers in Psychology, 12, 671615. https://doi.org/10.3389/FPSYG.2021.671615/BIBTEX
  • Kaplan, A. (2008). Clarifying metacognition, self-regulation, and self-regulated learning: What’s the purpose? Educational Psychology Review, 20(4), 477–484. https://doi.org/10.1007/s10648-008-9087-2
  • Khatib Zanjani, N., Ajam, A. A., & Badnava, S. (2017). Determining the relationship between self-directed learning readiness and acceptance of e-learning and academic achievement of students. Iran Journal of Nursing, 30(106), 11–22. https://doi.org/10.29252/ijn.30.106.11
  • Klímová, B., & Seraj, P. M. I. (2023). The use of chatbots in university EFL settings: Research trends and pedagogical implications. Frontiers in Psychology, 14(1131506), 1–7. https://doi.org/10.3389/fpsyg.2023.1131506
  • Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Prentice Hall/Cambridge.
  • Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. https://doi.org/10.1016/j.tourman.2021.104330
  • Kock, F., Josiassen, A., & Assaf, A. G. (2019). The xenophobic tourist. Annals of Tourism Research, 74, 155–166. https://doi.org/10.1016/j.annals.2018.11.005
  • Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302
  • Kranzow, J., & Hyland, N. (2016). Self-directed learning: Developing readiness in graduate students. International Journal of Self-Directed Learning, 13(2), 1–14.
  • Kulviwat, S., C. Bruner Ii, G., & P. Neelankavil, J. (2014). Self-efficacy as an antecedent of cognition and affect in technology acceptance. Journal of Consumer Marketing, 31(3), 190–199. https://doi.org/10.1108/JCM-10-2013-0727
  • Kumar, J. A. (2021). Educational chatbots for project-based learning: Investigating learning outcomes for a team-based design course. International Journal of Educational Technology in Higher Education, 18(1), 65. https://doi.org/10.1186/s41239-021-00302-w
  • Kumar, J. A., & Silva, P. A. (2020). Work-in-progress: A preliminary study on students’ acceptance of chatbots for studio-based learning. IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, (pp. 1627–1631). IEEE. https://doi.org/10.1109/EDUCON45650.2020.9125183
  • Laurillard, D. (2002). Rethinking university teaching: A conversational framework for the effective use of learning technologies. Routledge. https://doi.org/10.3390/su15010872
  • Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191(C), 104646. https://doi.org/10.1016/j.compedu.2022.104646
  • Limna, P. (2022). Artificial Intelligence (AI) in the hospitality industry: A review article. International Journal of Computing Sciences Research, 7, 1306–1317. https://doi.org/10.25147/ijcsr.2017.001.1.103
  • Lin, Y., & Yu, Z. (2023). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-12-2022-0165
  • Lin, X. (2023). Exploring the role of ChatGPT as a facilitator for motivating self-directed learning among adult learners. Adult Learning. https://doi.org/10.1177/1045159523118492
  • Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021). Adoption of Chatbots for learning among university students: Role of perceived convenience and enhanced performance. International Journal of Emerging Technologies in Learning, 16(18), 200–212. https://doi.org/10.3991/ijet.v16i18.24315
  • Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid pls-sem-neural network modelling approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726
  • Morris, T. H. (2019). Self-directed learning: A fundamental competence in a rapidly changing world. International Review of Education, 65(4), 633–653. https://doi.org/10.1007/s11159-019-09793-2
  • Murniati, C. T., Hartono, H., & Nugroho, A. C. (2022). Self-directed learning, self-efficacy, and technology readiness in e-learning among university students. KnE Social Sciences, 213–224. https://doi.org/10.18502/kss.v7i14.1197
  • Nalini, C., Kumari, R. S., Parteban, G. K., Priyaa, T. N., & Sanchay, A. S. (2021). AI based chatbot in food industry. In AIP Conference Proceedings (Vol. 2387, No. 1, p. 140040). AIP Publishing LLC. https://doi.org/10.1063/5.0069043
  • Nawaz, N., & Saldeen, M. A. (2020). Artificial intelligence chatbots for library reference services. Journal of Management Information & Decision Sciences, 23(S1), 442–449.
  • Nguyen, D. M., Chiu, Y. T. H., & Le, H. D. (2021). Determinants of continuance intention towards banks’ chatbot services in Vietnam: A necessity for sustainable development. Sustainability, 13(14), 7625. https://doi.org/10.3390/su13147625
  • Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139–4155. https://doi.org/10.1007/S10639-020-10157-9/TABLES/6
  • Okonkwo, C. W., & Ade-Ibijola, A. (2020). Python-Bot: A chatbot for teaching python programming. Engineering Letters, 29(1), 25–34.
  • Okuda, T., & Shoda, S. (2018). AI-based chatbot service for financial industry. Fujitsu Scientific and Technical Journal, 54(2), 4–8.
  • Paas, L. J., Dolnicar, S., & Karlsson, L. (2018). Instructional manipulation checks: A longitudinal analysis with implications for MTurk. International Journal of Research in Marketing, 35(2), 258–269. https://doi.org/10.1016/j.ijresmar.2018.01.003
  • Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 564294. https://doi.org/10.3389/fpsyg.2020.564294
  • Park, D. Y., & Kim, H. (2023). Determinants of intentions to use digital mental healthcare content among university students, faculty, and staff: Motivation, perceived usefulness, perceived ease of use, and parasocial interaction with AI chatbot. Sustainability, 15(1), 872. https://doi.org/10.3390/su15010872
  • Pereira, J. (2016). Leveraging chatbots to improve self-guided learning through conversational quizzes. In Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 911–918). https://doi.org/10.1145/3012430.3012625
  • Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549–1565. https://doi.org/10.1002/cae.22326
  • Pham, K. T., Nabizadeh, A., & Selek, S. (2022). Artificial intelligence and chatbots in psychiatry. The Psychiatric Quarterly, 93(1), 249–253. https://doi.org/10.1007/s11126-022-09973-8
  • Pijetlovic, D., & Mueller-Christ, G. (2022). HumanRoboLab: Experiments with chatbots in management education at universities. In Diginomics research perspectives: The role of digitalization in business and society (pp. 1–12). Springer International Publishing.
  • Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
  • Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452–502). Academic Press.
  • Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001
  • Ringle, C. M., Wende, S., Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.Smartpls.Com.
  • Saks, K., & Leijen, Ä. (2014). Distinguishing self-directed and self-regulated learning and measuring them in the e-learning context. Procedia - Social and Behavioral Sciences, 112, 190–198. https://doi.org/10.1016/j.sbspro.2014.01.1155
  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/j.chb.2016.09.061
  • Scherer, R., & Hatlevik, O. E. (2017). “Sore eyes and distracted” or “excited and confident”? The role of perceived negative consequences of using ICT for perceived usefulness and self-efficacy. Computers & Education, 115(C), 188–200. https://doi.org/10.1016/j.compedu.2017.08.003
  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189/FULL/PDF
  • Skrebeca, J., Kalniete, P., Goldbergs, J., Pitkevica, L., Tihomirova, D., & Romanovs, A. (2021). Modern development trends of chatbots using artificial intelligence (AI). In 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) (pp. 1–6). IEEE. https://doi.org/10.1109/ITMS52826.2021.9615258
  • Stephen, J. S., Rockinson-Szapkiw, A. J., & Dubay, C. (2020). Persistence model of non-traditional online learners: Self-efficacy, self-regulation, and self-direction. American Journal of Distance Education, 34(4), 306–321. https://doi.org/10.1080/08923647.2020.1745619
  • Tan, S. C., Divaharan, S., Tan, L. L. W., & Cheah, H. M. (2011). Self-directed learning with ICT: Theory, practice and assessment. Educational Technology Division, Ministry of Education.
  • The Jamovi Project. (2022). Jamovi (2.3). ttps://www.jamovi.org
  • Thorat, S. A., & Jadhav, V. (2020). A review on implementation issues of rule-based chatbot systems. Proceedings of the International Conference on Innovative Computing & Communications (ICICC). https://doi.org/10.2139/ssrn.3567047
  • Timothy, T., Seng Chee, T., Chwee Beng, L., Ching Sing, C., Joyce Hwee Ling, K., Wen Li, C., & Horn Mun, C. (2010). The self-directed learning with technology scale (SDLTS) for young students: An initial development and validation. Computers & Education, 55(4), 1764–1771. https://doi.org/10.1016/j.compedu.2010.08.001
  • Venkatesh, V., Raman, R., & Cruz-Jesus, F. (2023). AI and emerging technology adoption: A research agenda for operations management. International Journal of Production Research, 1–11. https://doi.org/10.1080/00207543.2023.2192309
  • Wube, H. D., Esubalew, S. Z., Weldesellasie, F. F., & Debelee, T. G. (2022). Text-based chatbot in financial sector: A systematic literature review. Data Science in Finance and Economics, 2(3), 232–259. https://doi.org/10.3934/DSFE.2022011
  • Xie, Y., Boudouaia, A., Xu, J., Al-Qadri, A. H., Khattala, A., Li, Y., & Aung, Y. M. (2023). A study on teachers’ continuance intention to use technology in English instruction in western China junior secondary schools. Sustainability, 15(5), 4307. https://doi.org/10.3390/su15054307
  • Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154–177. https://doi.org/10.1177/0735633120952067
  • Zheng, B. (2022). Medical students’ technology use for self-directed learning: Contributing and constraining factors. Medical Science Educator, 32(1), 149–156. https://doi.org/10.1177/0735633120952067

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