Students’ Response to ChatGPT: An Adaptive Technology-To-Performance Model

References

• Khan R, Jawaid M, Khan A, Sajjad M. ChatGPT - reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):605–607. doi:10.12669/pjms.39.2.7653.
   PubMed Web of Science ®Google Scholar
• Pratt DD. Good teaching: one size fits all? New Dir Adult Contin Educ. 2002;2002(93):5–16. doi:10.1002/ace.45.
   Google Scholar
• Prykhodko D. Current challenges and prospects of using artificial intelligence technologies in contemporary education. Eur Sci. 2024;(sge27–02):132–162. doi:10.30890/2709-2313.2024-27-00-038.
   Google Scholar
• Budhwar P, Chowdhury S, Wood G, Aguinis H, Bamber GJ, Beltran JR, Boselie P, Lee Cooke F, Decker S, DeNisi A, et al. Human resource management in the age of generative artificial intelligence: perspectives and research directions on ChatGPT. Hum Res Mgmt J. 2023;33(3):606–659. doi:10.1111/1748-8583.12524.
   Google Scholar
• Alshahrani A. The impact of ChatGPT on blended learning: current trends and future research directions. 105267/J.Ijdns. 2023;7(4):2029–2040. doi:10.5267/j.ijdns.2023.6.010.
   Google Scholar
• Al-Qaysi N, Al-Emran M, Al-Sharafi MA, Iranmanesh M, Ahmad A, Mahmoud MA. Determinants of ChatGPT use and its impact on learning performance: an integrated Model of BRT and TPB. Int J Hum–Comput Interact. 2024; 1–13. doi:10.1080/10447318.2024.2361210.
   Google Scholar
• Fan Y, Tan Y, Raković M, Wang Y, Cai Z, Shaffer DW, Gašević D. Dissecting learning tactics in MOOC using ordered network analysis. Comput Assisted Learn. 2023;39(1):154–166. doi:10.1111/jcal.12735.
   Google Scholar
• Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L. Generative AI and ChatGPT: applications, challenges, and ai-human collaboration. J Inf Technol Case Appl Res. 2023;25(3):277–304. doi:10.1080/15228053.2023.2233814.
   Google Scholar
• Tassoti S. Assessment of students use of generative artificial intelligence: prompting strategies and prompt engineering in chemistry education. J Chem Educ. 2024;101(6):2475–2482. doi:10.1021/acs.jchemed.4c00212.
   Google Scholar
• Lewis TG. The many faces of resilience. Commun ACM. 2022;66(1):56–61. doi:10.1145/3519262.
   Google Scholar
• Schenk B, Hoxhaj L. Challenges in switching to blended learning environments: an analysis of students’ attitudes and performance. ICIS 2019 Proceedings; 2019 Nov 8. https://aisel.aisnet.org/icis2019/learning_environ/learning_environ/5.
   Google Scholar
• Kooli C. Chatbots in education and research: a critical examination of ethical implications and solutions. Sustainability. 2023;15(7):5614. doi:10.3390/su15075614.
   Web of Science ®Google Scholar
• Zhu JJ, Jiang J, Yang M, Ren ZJ. ChatGPT and environmental research. Environ Sci Technol. 2023;57(46):17667–17670. doi:10.1021/acs.est.3c01818.
   PubMedGoogle Scholar
• Li J, Ren X, Jiang X, Chen CH. Exploring the use of ChatGPT in Chinese language classrooms. Int J Chin Lang Teach. 2023;4(3):36–55.
   Google Scholar
• van Dinter R, Catal C, Tekinerdogan B. A decision support system for automating document retrieval and citation screening. Expert Syst Appl. 2021;182:115261. doi:10.1016/j.eswa.2021.115261.
   Google Scholar
• Wagner G, Lukyanenko R, Paré G. Artificial intelligence and the conduct of literature reviews. J Inf Technol. 2022;37(2):209–226. doi:10.1177/02683962211048201.
   Google Scholar
• Zhang F, Schunn C, Chen S, Li W, Li R. EFL student engagement with giving peer feedback in academic writing: a longitudinal study. J Engl Acad Purp. 2023;64:101255. doi:10.1016/j.jeap.2023.101255.
   Google Scholar
• Lim VF. ChatGPT raises uncomfortable questions about teaching and classroom learning | the straits times. 2022 [accessed 2023 Dec 15]. https://www.straitstimes.com/opinion/need-to-review-literacy-assessment-in-the-age-of-chatgpt.
   Google Scholar
• McMurtrie B. Teaching: Will ChatGPT change the way you teach? The chronicle of higher education. 2023 Jan 5 [accessed 2023 Dec 14]. https://www.chronicle.com/newsletter/teaching/2023-01-05.
   Google Scholar
• Fishbein M. A theory of reasoned action: some applications and implications. Neb Symp Motivation. 1979;27:65–116.
   Google Scholar
• Ajzen I. Behavioral interventions: design and evaluation guided by the theory of planned behavior. In: Mark MM, Donaldson SI, Campbell B, editors. Social psychology and evaluation. The Guilford Press; 2011. p. 72–101.
   Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. Mis Q. 2003;27(3):425–478. doi:10.2307/30036540.
   Web of Science ®Google Scholar
• Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci. 2000;46(2):186–204. doi:10.1287/mnsc.46.2.186.11926.
   Web of Science ®Google Scholar
• Olson MH, Ives B. Chargeback systems and user involvement in information systems - an empirical investigation. Mis Q. 1982;6(2):47–60. doi:10.2307/249282.
   Web of Science ®Google Scholar
• Choung H, David P, Ross A. Trust in AI and Its role in the acceptance of AI technologies. Int J Hum–Comput Interact. 2023;39(9):1727–1739. doi:10.1080/10447318.2022.2050543.
   Google Scholar
• Wang Y, Wang S, Wang J, Wei J, Wang C. An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model. Transportation. 2020;47(1):397–415. doi:10.1007/s11116-018-9893-4.
   Web of Science ®Google Scholar
• Goodhue DL, Thompson RL. Task-technology fit and individual performance. Mis Q. 1995;19(2):213–236. doi:10.2307/249689.
   Web of Science ®Google Scholar
• Melville N, Kraemer K, Gurbaxani V. Information technology and organizational performance: an integrative model of it business value. Mis Q. 2004;28(2):283–322. doi:10.2307/25148636.
   Web of Science ®Google Scholar
• Zhang P, Tur G. A systematic review of ChatGPT use in K-12 education. Euro J Educ. 2024;59(2):e12599. doi:10.1111/ejed.12599.
   Google Scholar
• Opara E, Mfon-Ette Theresa A, Aduke TC. ChatGPT for teaching, learning and research: prospects and challenges. Opara Emmanuel Chinonso, Adalikwu Mfon-Ette Theresa, Tolorunleke Caroline Aduke (2023) ChatGPT for teach, learn and res: prospects and challenges. Glob Acad J Humanit Soc Sci. 2023 [accessed 2024 Jul 20]. 5. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4375470.
   Google Scholar
• Kalla D, Smith N, Samaah F, Kuraku S. Study and analysis of chat GPT and its impact on different fields of study. Int J Innovative Sci Res Technol. 2023 [accessed 2024 Jul 20]. 8(3). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4402499.
   Google Scholar
• Roumeliotis KI, Tselikas ND. Chatgpt and open-ai models: a preliminary review. Future Internet. 2023;15(6):192. doi:10.3390/fi15060192.
   Web of Science ®Google Scholar
• Venkatesh V, Speier C, Morris MG. User acceptance enablers in individual decision making about technology: toward an integrated model. Decis Sci. 2002;33(2):297–316. doi:10.1111/j.1540-5915.2002.tb01646.x.
   Web of Science ®Google Scholar
• Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008;39(2):273–315. doi:10.1111/j.1540-5915.2008.00192.x.
   Web of Science ®Google Scholar
• Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78. doi:10.1037/0003-066X.55.1.68.
   PubMed Web of Science ®Google Scholar
• Lee MK, Cheung CM, Chen Z. Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Inf Manag. 2005;42(8):1095–1104. doi:10.1016/j.im.2003.10.007.
   Web of Science ®Google Scholar
• Alqurashi F. Learning strategies in L2 settings in Saudi Arabia: an annotated bibliography. Int J Appl Ling Engl Lit. 2018;7(7):17–26. doi:10.7575/aiac.ijalel.v.7n.7p.17.
   Google Scholar
• Csikszentmihalyi M. Flow : the psychology of optimal experience. (NY): Harper & Row; 1990 [accessed 2023 Dec 15]. http://archive.org/details/flowpsychologyof00csik.
   Google Scholar
• Pekrun R, Goetz T, Titz W, Perry RP. Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educ Psychol. 2002;37(2):91–105. doi:10.1207/S15326985EP3702_4.
   Web of Science ®Google Scholar
• Linnenbrink EA, Pintrich PR. Motivation as an enabler for academic success. Sch Psychol Rev. 2002;31(3):313–327. doi:10.1080/02796015.2002.12086158.
   Web of Science ®Google Scholar
• Daniels LM, Haynes TL, Stupnisky RH, Perry RP, Newall NE, Pekrun R. Individual differences in achievement goals: a longitudinal study of cognitive, emotional, and achievement outcomes. Contemp Educ Phychol. 2008;33(4):584–608. doi:10.1016/j.cedpsych.2007.08.002.
   Web of Science ®Google Scholar
• Lee JH, Shin D, Noh W. Artificial intelligence-based content generator technology for young English-as-a-foreign-language learners’ reading enjoyment. Relc J. 2023;54(2):508–516. doi:10.1177/00336882231165060.
   Google Scholar
• Liu G, Darvin R, Ma C. Unpacking the role of motivation and enjoyment in ai-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation in the Chinese context. Comput Hum Behav. 2024;160:108362. doi:10.1016/j.chb.2024.108362.
   Google Scholar
• Holmes W, Bialik M, Fadel C. Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign; 2019 [accessed 2024 Jul 20]. https://discovery.ucl.ac.uk/id/eprint/10139722/.
   Google Scholar
• Espinosa-Curiel IE, Pozas-Bogarin EE, Martínez-Miranda J, Pérez-Espinosa H. Relationship between children’s enjoyment, user experience satisfaction, and learning in a serious video game for nutrition education: empirical pilot study. JMIR Serious Games. 2020;8(3):e21813. doi:10.2196/21813.
   PubMedGoogle Scholar
• Mattas PS. ChatGPT: a study of AI language processing and its implications. Int J Res Publ Rev. 2023;4(2):435–440. doi:10.55248/gengpi.2023.4218.
   Google Scholar
• Krishnan C, Gupta A, Gupta A, Singh G. Impact of artificial intelligence-based chatbots on customer engagement and business growth. In: Hong T, Serrano-Estrada L, Saxena A Biswas A. editors. Deep learning for social media data analytics. Studies in Big Data. Springer International Publishing; 2022. p. 195–210. doi:10.1007/978-3-031-10869-3_11.
   Google Scholar
• Valverde-Berrocoso J, Acevedo-Borrega J, Cerezo-Pizarro M. Educational technology and student performance: a systematic review. Front Educ. 2022 [accessed 2023 Oct 25]. 7. doi:10.3389/feduc.2022.916502.
   Google Scholar
• Al-Abdullatif AM, Gameil AA. The effect of digital technology integration on students’ academic performance through project-based learning in an E-learning environment. Int J Emerg Technol Learn. 2021;16(11):189–210. doi:10.3991/ijet.v16i11.19421.
   Google Scholar
• Limniou M. The effect of digital device usage on student academic performance: a case study. Educ Sci. 2021;11(3):121. doi:10.3390/educsci11030121.
   Google Scholar
• Currie GM. Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy? Semin Nucl Med. 2023;53(5):719–730. doi:10.1053/j.semnuclmed.2023.04.008.
   PubMed Web of Science ®Google Scholar
• Bhattacharyya M, Miller VM, Bhattacharyya D, Miller LE. High rates of fabricated and inaccurate references in ChatGPT-generated medical content. Cureus. 2023;15(5):e39238. doi:10.7759/cureus.39238.
   PubMedGoogle Scholar
• Cavite FAM, Marcial DE. Correlates of learning satisfaction and learning engagement in online distance education. Inf Technol Learn Tools. 2022;90(4):118–135. doi:10.33407/itlt.v90i4.4920.
   Google Scholar
• Sotelo Muñoz S, Gayoso G, Huambo A, Tapia RD, Incaluque JL, Aguila OE, Cajamarca JC, Acevedo JE, Rivera HV, Arias-Gonzáles, JL. Examining the impacts of ChatGPT on Student motivation and engagement. Przestrzeń Społeczna (Soc Space). 2023;23(1):1–27.
   Google Scholar
• Almanasreh E, Moles R, Chen TF. Evaluation of methods used for estimating content validity. Res Soc Adm Pharm. 2019;15(2):214–221. doi:10.1016/j.sapharm.2018.03.066.
   PubMed Web of Science ®Google Scholar
• Fornell C. A national customer satisfaction barometer: the Swedish experience. J Mark. 1992;56(1):6–21. doi:10.1177/002224299205600103.
   Web of Science ®Google Scholar
• Torkzadeh G, Doll WJ. The development of a tool for measuring the perceived impact of information technology on work. Omega. 1999;27(3):327–339. doi:10.1016/S0305-0483(98)00049-8.
   Web of Science ®Google Scholar
• McKnight DH, Choudhury V, Kacmar C. Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res. 2002;13(3):334–359. doi:10.1287/isre.13.3.334.81.
   Web of Science ®Google Scholar
• Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace 1. J Appl Soc Pyschol. 1992;22(14):1111–1132. doi:10.1111/j.1559-1816.1992.tb00945.x.
   Web of Science ®Google Scholar
• Hulland J, Baumgartner H, Smith KM. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. J Acad Mark Sci. 2018;46(1):92–108. doi:10.1007/s11747-017-0532-y.
   Web of Science ®Google Scholar
• Perneger TV, Courvoisier DS, Hudelson PM, Gayet-Ageron A. Sample size for pre-tests of questionnaires. Qual Life Res. 2015;24(1):147–151. doi:10.1007/s11136-014-0752-2.
   PubMed Web of Science ®Google Scholar
• Chae H-C, Kim G, Koh CE. Impact of privacy behaviors on relatedness and well-being in SNSs. J Comput Inf Syst. 2023;63(1):228–239. doi:10.1080/08874417.2022.2052378.
   Google Scholar
• Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334. doi:10.1007/BF02310555.
   Web of Science ®Google Scholar
• Armstrong JS, Overton TS. Estimating nonresponse bias in mail surveys. J Mark Res. 1977;14(3):396–402. doi:10.1177/002224377701400320.
   Web of Science ®Google Scholar
• Nunnally J, Bernstein I. Psychometric theory. 3rd ed. New York: McGraw-Hill.; 1994.
   Google Scholar
• Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50. doi:10.1177/002224378101800104.
   Web of Science ®Google Scholar
• Hair J, Babin B, Anderson R, Clark E. Multivariate data analysis. 8th ed. Boston: Cengage Learning EMEA; 2018.
   Google Scholar
• Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;56(2):81. doi:10.1037/h0046016.
   PubMed Web of Science ®Google Scholar
• Harman HH. Modern factor analysis. Chicago: University of Chicago Press; 1976.
   Google Scholar
• Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879–903. doi:10.1037/0021-9010.88.5.879.
   PubMed Web of Science ®Google Scholar
• Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociol Methods Res. 1992;21(2):230–258. doi:10.1177/0049124192021002005.
   Web of Science ®Google Scholar
• Braun J, Van der Beek P, Batt G. Quantitative thermochronology: numerical methods for the interpretation of thermochronological data. Cambridge University Press; 2006 [accessed 2024 Jul 20]. https://books.google.com/books?hl=en&lr=&id=bOciYxRU8fUC&oi=fnd&pg=PA208&dq=Braun+et.al+2006&ots=e073h6Jkn8&sig=YDceSbKzuC-gaFTTCK-4mYtrRRE.
   Google Scholar
• Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46. doi:10.1177/001316446002000104.
   Web of Science ®Google Scholar
• Pereira R, Tam C. Impact of enjoyment on the usage continuance intention of video-on-demand services. Inf Manag. 2021;58(7):103501. doi:10.1016/j.im.2021.103501.
   Google Scholar
• Lu L, Guo X, Zhao J. A unified nonlocal strain gradient model for nanobeams and the importance of higher order terms. Int J Eng Sci. 2017;119:265–277. doi:10.1016/j.ijengsci.2017.06.024.
   Web of Science ®Google Scholar
• Jarosz E. What makes life enjoyable at an older age? Experiential wellbeing, daily activities, and satisfaction with life in general. Aging Ment Health. 2022;26(6):1242–1252. doi:10.1080/13607863.2021.1916879.
   PubMed Web of Science ®Google Scholar
• Ye J, Dong B, Lee JY. The long-term impact of service empathy and responsiveness on customer satisfaction and profitability: a longitudinal investigation in a healthcare context. Mark Lett. 2017;28(4):551–564. doi:10.1007/s11002-017-9429-2.
   Google Scholar
• World Economic Forum Annual Meeting. Davos. World Economic Forum; 2023 [accessed 2024 Jul 20]. https://www.weforum.org/events/world-economic-forum-annual-meeting-2023/.
   Google Scholar
• Spivakovsky OV, Omelchuk SA, Kobets VV, Valko NV, Malchykova DS. Institutional policies on artificial intelligence in university learning, teaching and research. Inf Technol Learn Tools. 2023;97(5):181–202. doi:10.33407/itlt.v97i5.5395.
   Google Scholar
• Fauzi F, Tuhuteru L, Sampe F, Ausat AMA, Hatta HR. Analysing the role of ChatGPT in improving student productivity in higher education. J Educ. 2023;5(4):14886–14891. doi:10.31004/joe.v5i4.2563.
   Google Scholar
• Imran M, Almusharraf N. Analyzing the role of ChatGPT as a writing assistant at higher education level: a systematic review of the literature. CONT ED Technol. 2023;15(4):ep464. doi:10.30935/cedtech/13605.
   Google Scholar
• Surameery NMS, Shakor MY. Use chat GPT to solve programming bugs. Int J Inf Technol Comput Eng. 2023;3(31):17–22. doi:10.55529/ijitc.31.17.22.
   Google Scholar
• Monteith S, Glenn T, Geddes JR, Whybrow PC, Achtyes E, Bauer M. Artificial intelligence and increasing misinformation. The Br J Psychiatry. 2023 Oct 26. 1–3. doi:10.1192/bjp.2023.136.
   Google Scholar
• Tyson J. Shortcomings of ChatGPT. J Chem Educ. 2023;100(8):3098–3101. doi:10.1021/acs.jchemed.3c00361.
   Google Scholar