Prioritizing Ethical Conundrums in the Utilization of ChatGPT in Education through an Analytical Hierarchical Approach
Abstract
:1. Introduction
2. Review of Related Studies
2.1. Taxonomy of ChatGPT Utilization in Education
2.2. Theoretical Models of Prior Research
2.3. Methodologies of Prior Research
2.4. Research Gap and Motivation
3. Theoretical Background and Framework
3.1. Risk
3.2. Reward
3.3. Resilience
3.4. Summary and Conceptual Framework Based on AHP
4. Research Design and Methodology
4.1. Building Pairwise Comparison Matrices
4.2. Construction of Aggregate Comparison Matrices
4.3. Computation of RRR Theme Relative Weights
4.4. Validation of the Comparison Matrix through Consistency Test
- Step 1: Calculating the principal Eigenvalue by using Equation (6):
- Step 2: Calculate the , where is the consistency index given by Equation (7):
- Step 3: Calculate the random index (), where represents the predefined value determined by the matrix order (n). It is acquired from a reference table corresponding to the matrix order, resulting in distinct values of for different numbers of criteria (n), as outlined in Table 5.
- Step 4: Calculating CR by using Equation (8):
- Step 5: Verify the acceptance of . If the is equal to or less than , it signifies that the level of inconsistency within the comparison matrix A is acceptable, and the reliability of the ranking results can be affirmed. Among the 12 responses, only 10 passed the consistency test; therefore, only those are reported in this study.
4.5. Computation of Global Weights of the RRR Themes
5. Results and Discussion
5.1. RRR Normalized Matrix and Weight
5.2. Normalized Matrix and Weight of Risk Themes
5.3. Normalized Matrix and Weight of Reward Themes
5.4. Normalized Matrix and Weight of Resilience Themes
5.5. Global Weights
5.6. Discussion
6. Contribution and Implications
6.1. Theoretical Contributions
6.2. Practical Implications
6.3. Limitations and Future Work
6.4. Next Steps
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- George, B.; Wooden, O. Managing the strategic transformation of higher education through artificial intelligence. Adm. Sci. 2023, 13, 196. [Google Scholar] [CrossRef]
- Kumar, D. How Emerging Technologies are Transforming Education and Research: Trends, Opportunities, and Challenges. In Infinite Horizons: Exploring the Unknown; CIRS Publication: Patna, India, 2023; p. 89. [Google Scholar]
- Tan, S. Harnessing Artificial Intelligence for innovation in education. In Learning Intelligence: Innovative and Digital Transformative Learning Strategies: Cultural and Social Engineering Perspectives; Springer Nature: Singapore, 2023; pp. 335–363. [Google Scholar]
- Natriello, G.; Chae, H. The Paradox of Learning in the Intelligence Age: Creating a New Learning Ecosystem to Meet the Challenge. In Bridging Human Intelligence and Artificial Intelligence; Springer International Publishing: Cham, Switzerland, 2022; pp. 287–300. [Google Scholar]
- Michel-Villarreal, R.; Vilalta-Perdomo, E.; Salinas-Navarro, D.; Thierry-Aguilera, R.; Gerardou, F. Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Educ. Sci. 2023, 13, 856. [Google Scholar] [CrossRef]
- Farrokhnia, M.; Banihashem, S.; Noroozi, O.; Wals, A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innov. Educ. Teach. Int. 2023, 61, 460–474. [Google Scholar] [CrossRef]
- Sallam, M. ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare 2023, 11, 887. [Google Scholar] [CrossRef] [PubMed]
- Cotton, D.; Cotton, P.; Shipway, J. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int. 2023, 61, 228–239. [Google Scholar] [CrossRef]
- Su, J.; Yang, W. Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Rev. Educ. 2023, 6, 355–366. [Google Scholar]
- Liebrenz, M.; Schleifer, R.; Buadze, A.; Bhugra, D.; Smith, A. Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. Lancet Digit. Health 2023, 5, 105–106. [Google Scholar] [CrossRef] [PubMed]
- Tlili, A.; Shehata, B.; Adarkwah, M.; Bozkurt, A.; Hickey, D.; Huang, R.; Agyemang, B. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 2023, 10, 15. [Google Scholar] [CrossRef]
- Lee, H. The rise of ChatGPT: Exploring its potential in medical education. Anat. Sci. Educ. 2023, 17, 926–931. [Google Scholar] [CrossRef]
- Pavlik, J. Collaborating with ChatGPT: Considering the Implications of Generative Artificial. J. Mass Commun. Educ. 2023, 78, 84–93. [Google Scholar]
- Lund, B.; Wang, T.; Mannuru, N.; Nie, B.; Shimray, S.; Wang, Z. ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. J. Assoc. Inf. Sci. Technol. 2023, 74, 570–581. [Google Scholar] [CrossRef]
- Dwivedi, Y.; Kshetri, N.; Hughes, L.; Slade, E.; Jeyaraj, A.; Kar, A.; Wright, R. So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642. [Google Scholar] [CrossRef]
- Lim, W.; Gunasekara, A.; Pallant, J.; Pallant, J.; Pechenkina, E. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. Int. J. Manag. Educ. 2023, 21, 100790. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sayeed, M.S.; Razak, S.F.A.; Yogarayan, S.; Amodu, O.A. An integrative decision-making framework to guide policies on regulating ChatGPT usage. PeerJ Comput. Sci. 2024, 10, e1845. [Google Scholar] [CrossRef]
- Bukar, U.A.; Jabar, M.A.; Sidi, F.; Nor, R.N.H.B.; Abdullah, S.; Othman, M. Crisis informatics in the context of social media crisis communication: Theoretical models, taxonomy, and open issues. IEEE Access 2020, 8, 185842–185869. [Google Scholar] [CrossRef]
- Zhang, L.; Glänzel, W. Proceeding papers in journals versus the “regular” journal publications. J. Inf. 2012, 6, 88–96. [Google Scholar] [CrossRef]
- Zhang, Y.; Jia, X. Republication of conference papers in journals? Learn. Publ. 2013, 26, 189–196. [Google Scholar] [CrossRef]
- Montesi, M.; Owen, J.M. From conference to journal publication: How conference papers in software engineering are extended for publication in journals. J. Am. Soc. Inf. Sci. Technol. 2008, 59, 816–829. [Google Scholar] [CrossRef]
- Perkins, M. Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. J. Univ. Teach. Learn. Pract. 2023, 20, 07. [Google Scholar] [CrossRef]
- Kooli, C. Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability 2023, 15, 5614. [Google Scholar] [CrossRef]
- Qasem, F. ChatGPT in scientific and academic research: Future fears and reassurances. Libr. Tech News 2023, 40, 30–32. [Google Scholar] [CrossRef]
- Ariyaratne, S.; Iyengar, K.; Nischal, N.; Chitti Babu, N.; Botchu, R. A comparison of ChatGPT-generated articles with human-written articles. Skelet. Radiol. 2023, 52, 1755–1758. [Google Scholar] [CrossRef]
- Yan, D. Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Educ. Inf. Technol. 2023, 28, 13943–13967. [Google Scholar] [CrossRef]
- Ray, P. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things-Cyber-Phys. Syst. 2023, 3, 121–154. [Google Scholar] [CrossRef]
- Taecharungroj, V. What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data Cogn. Comput. 2023, 7, 35. [Google Scholar] [CrossRef]
- Grünebaum, A.; Chervenak, J.; Pollet, S.; Katz, A.; Chervenak, F. The exciting potential for ChatGPT in obstetrics and gynecology. Am. J. Obstet. Gynecol. 2023, 228, 696–705. [Google Scholar] [CrossRef]
- Cox, C.; Tzoc, E. ChatGPT: Implications for academic libraries. Coll. Res. Libr. News 2023, 84, 99. [Google Scholar] [CrossRef]
- Karaali, G. Artificial Intelligence, Basic Skills, and Quantitative Literacy. Numeracy 2023, 16, 9. [Google Scholar] [CrossRef]
- Jungwirth, D.; Haluza, D. Artificial intelligence and public health: An exploratory study. Int. J. Environ. Res. Public Health 2023, 20, 4541. [Google Scholar] [CrossRef]
- Geerling, W.; Mateer, G.; Wooten, J.; Damodaran, N. ChatGPT has aced the test of understanding in college economics: Now what? Am. Econ. 2023, 68, 233–245. [Google Scholar] [CrossRef]
- Supianto; Widyaningrum, R.; Wulandari, F.; Zainudin, M.; Athiyallah, A.; Rizqa, M. Exploring the factors affecting ChatGPT acceptance among university students. Multidiscip. Sci. J. 2024, 6, 2024273. [Google Scholar] [CrossRef]
- Alshammari, S.H.; Alshammari, M.H. Factors Affecting the Adoption and Use of ChatGPT in Higher Education. Int. J. Inf. Commun. Technol. Educ. 2024, 20, 1–16. [Google Scholar] [CrossRef]
- Strzelecki, A.; Cicha, K.; Rizun, M.; Rutecka, P. Acceptance and use of ChatGPT in the academic community. Educ. Inf. Technol. 2024, 1–26. [Google Scholar] [CrossRef]
- Strzelecki, A.; ElArabawy, S. Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. Br. J. Educ. Technol. 2024, 55, 1209–1230. [Google Scholar] [CrossRef]
- Elkefi, S.; Tounsi, A.; Kefi, M.A. Use of ChatGPT for education by engineering students in developing countries: A mixed-methods study. Behav. Inf. Technol. 2024, 1–17. [Google Scholar] [CrossRef]
- Bouteraa, M.; Bin-Nashwan, S.A.; Al-Daihani, M.; Dirie, K.A.; Benlahcene, A.; Sadallah, M.; Zaki, H.O.; Lada, S.; Ansar, R.; Fook, L.M.; et al. Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students’ integrity matter? Comput. Hum. Behav. Rep. 2024, 14, 100402. [Google Scholar] [CrossRef]
- Bhat, M.A.; Tiwari, C.K.; Bhaskar, P.; Khan, S.T. Examining ChatGPT adoption among educators in higher educational institutions using extended UTAUT model. J. Inf. Commun. Ethics Soc. 2024; ahead-of-print. [Google Scholar]
- Arthur, F.; Salifu, I.; Abam Nortey, S. Predictors of higher education students’ behavioural intention and usage of ChatGPT: The moderating roles of age, gender and experience. Interact. Learn. Environ. 2024, 1–27. [Google Scholar] [CrossRef]
- Grassini, S.; Aasen, M.L.; Møgelvang, A. Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model. Appl. Artif. Intell. 2024, 38, 2371168. [Google Scholar] [CrossRef]
- Salifu, I.; Arthur, F.; Arkorful, V.; Abam Nortey, S.; Solomon Osei-Yaw, R. Economics students’ behavioural intention and usage of ChatGPT in higher education: A hybrid structural equation modelling-artificial neural network approach. Cogent Soc. Sci. 2024, 10, 2300177. [Google Scholar] [CrossRef]
- Elshaer, I.A.; Hasanein, A.M.; Sobaih, A.E.E. The Moderating Effects of Gender and Study Discipline in the Relationship between University Students’ Acceptance and Use of ChatGPT. Eur. J. Investig. Heal. Psychol. Educ. 2024, 14, 1981–1995. [Google Scholar] [CrossRef]
- Gustilo, L.; Ong, E.; Lapinid, M.R. Algorithmically-driven writing and academic integrity: Exploring educators’ practices, perceptions, and policies in AI era. Int. J. Educ. Integr. 2024, 20, 3. [Google Scholar] [CrossRef]
- Kajiwara, Y.; Kawabata, K. AI literacy for ethical use of chatbot: Will students accept AI ethics? Comput. Educ. Artif. Intell. 2024, 6, 100251. [Google Scholar] [CrossRef]
- Cambra-Fierro, J.J.; Blasco, M.F.; López-Pérez, M.E.E.; Trifu, A. ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Educ. Inf. Technol. 2024, 1–22. [Google Scholar] [CrossRef]
- Tiwari, C.K.; Bhat, M.A.; Khan, S.T.; Subramaniam, R.; Khan, M.A.I. What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interact. Technol. Smart Educ. 2024, 21, 333–355. [Google Scholar] [CrossRef]
- Abdalla, R.A. Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT. Personalization as a moderator. J. Open Innov. Technol. Mark. Complex. 2024, 10, 100327. [Google Scholar] [CrossRef]
- Abdaljaleel, M.; Barakat, M.; Alsanafi, M.; Salim, N.A.; Abazid, H.; Malaeb, D.; Mohammed, A.H.; Hassan, B.A.R.; Wayyes, A.M.; Farhan, S.S.; et al. A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Sci. Rep. 2024, 14, 1983. [Google Scholar] [CrossRef] [PubMed]
- Sallam, M.; Salim, N.A.; Barakat, M.; Al-Mahzoum, K.; Al-Tammemi, A.B.; Malaeb, D.; Hallit, R.; Hallit, S. Assessing Health Students’ Attitudes and Usage of ChatGPT in Jordan: Validation Study. JMIR Med Educ. 2023, 9, e48254. [Google Scholar] [CrossRef]
- Sevnarayan, K.; Potter, M.A. Generative Artificial Intelligence in distance education: Transformations, challenges, and impact on academic integrity and student voice. J. Appl. Learn. Teach. 2024, 7, 104–114. [Google Scholar]
- García-Alonso, E.M.; León-Mejía, A.C.; Sánchez-Cabrero, R.; Guzmán-Ordaz, R. Training and Technology Acceptance of ChatGPT in University Students of Social Sciences: A Netcoincidental Analysis. Behav. Sci. 2024, 14, 612. [Google Scholar] [CrossRef]
- Masa’deh, R.; Majali, S.A.L.; Alkhaffaf, M.; Thurasamy, R.; Almajali, D.; Altarawneh, K.; Al-Sherideh, A.; Altarawni, I. Antecedents of adoption and usage of ChatGPT among Jordanian university students: Empirical study. Int. J. Data Netw. Sci. 2024, 8, 1099–1110. [Google Scholar] [CrossRef]
- Dahri, N.A.; Yahaya, N.; Al-Rahmi, W.M.; Aldraiweesh, A.; Alturki, U.; Almutairy, S.; Shutaleva, A.; Soomro, R.B. Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon 2024, 10, e29317. [Google Scholar] [CrossRef] [PubMed]
- Rahman, M.S.; Sabbir, M.M.; Zhang, J.; Moral, I.H.; Hossain, G.M.S. Examining students’ intention to use ChatGPT: Does trust matter? Australas. J. Educ. Technol. 2023, 39, 51–71. [Google Scholar] [CrossRef]
- Maheshwari, G. Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Educ. Inf. Technol. 2024, 29, 12167–12195. [Google Scholar] [CrossRef]
- 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. 2024; ahead-of-print. [Google Scholar] [CrossRef]
- Duong, C.D.; Vu, T.N.; Ngo, T.V.N. Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. Int. J. Manag. Educ. 2023, 21, 100883. [Google Scholar] [CrossRef]
- Alrishan, A.M.H. Determinants of Intention to Use ChatGPT for Professional Development among Omani EFL Pre-service Teachers. Int. J. Learn. Teach. Educ. Res. 2023, 22, 187–209. [Google Scholar] [CrossRef]
- Duong, C.D.; Nguyen, T.H.; Ngo, T.V.N.; Dao, V.T.; Do, N.D.; Pham, T.V. Exploring higher education students’ continuance usage intention of ChatGPT: Amalgamation of the information system success model and the stimulus-organism-response paradigm. Int. J. Inf. Learn. Technol. 2024; ahead-of-print. [Google Scholar]
- Mandai, K.; Tan, M.J.H.; Padhi, S.; Pang, K.T. A Cross-Era Discourse on ChatGPT’s Influence in Higher Education through the Lens of John Dewey and Benjamin Bloom. Educ. Sci. 2024, 14, 64. [Google Scholar] [CrossRef]
- Jochim, J.; Lenz-Kesekamp, V.K. Teaching and testing in the era of text-generative AI: Exploring the needs of students and teachers. Inf. Learn. Sci. 2024; ahead-of-print. [Google Scholar]
- Abdalla, A.A.; Bhat, M.A.; Tiwari, C.K.; Khan, S.T.; Wedajo, A.D. Exploring ChatGPT adoption among business and management students through the lens of diffusion of Innovation Theory. Comput. Educ. Artif. Intell. 2024, 7, 100257. [Google Scholar] [CrossRef]
- Mahmud, A.; Sarower, A.H.; Sohel, A.; Assaduzzaman, M.; Bhuiyan, T. Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach. Array 2024, 21, 100339. [Google Scholar] [CrossRef]
- Gupta, P.; Mahajan, R.; Badhera, U.; Kushwaha, P. Integrating generative AI in management education: A mixed-methods study using social construction of technology theory. Int. J. Manag. Educ. 2024, 22, 101017. [Google Scholar] [CrossRef]
- Al-Mughairi, H.; Bhaskar, P. Exploring the factors affecting the adoption AI techniques in higher education: Insights from teachers’ perspectives on ChatGPT. J. Res. Innov. Teach. Learn. 2024; ahead-of-print. [Google Scholar]
- Qu, K.; Wu, X. ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Educ. Inf. Technol. 2024, 1–33. [Google Scholar] [CrossRef]
- Crawford, J.; Allen, K.A.; Pani, B.; Cowling, M. When artificial intelligence substitutes humans in higher education: The cost of loneliness, student success, and retention. Stud. High. Educ. 2024, 49, 883–897. [Google Scholar] [CrossRef]
- Ngo, T.T.A.; Tran, T.T.; An, G.K.; Nguyen, P.T. ChatGPT for Educational Purposes: Investigating the Impact of Knowledge Management Factors on Student Satisfaction and Continuous Usage. IEEE Trans. Learn. Technol. 2024, 17, 1367–1378. [Google Scholar] [CrossRef]
- Okulu, H.Z.; Muslu, N. Designing a course for pre-service science teachers using ChatGPT: What ChatGPT brings to the table. Interact. Learn. Environ. 2024, 1–18. [Google Scholar] [CrossRef]
- Jangjarat, K.; Kraiwanit, T.; Limna, P.; Sonsuphap, R. Public Perceptions towards ChatGPT as the Robo-Assistant. Online J. Commun. Media Technol. 2023, 13, e202338. [Google Scholar] [CrossRef]
- Komba, M.M. The influence of ChatGPT on digital learning: Experience among university students. Glob. Knowledge, Mem. Commun. 2024; ahead-of-print. [Google Scholar]
- Espartinez, A.S. Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Comput. Educ. Artif. Intell. 2024, 7, 100264. [Google Scholar] [CrossRef]
- Sun, D.; Boudouaia, A.; Zhu, C.; Li, Y. Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. Int. J. Educ. Technol. High. Educ. 2024, 21, 14. [Google Scholar] [CrossRef]
- Sánchez-Ruiz, L.M.; Moll-López, S.; Nuñez-Pérez, A.; Moraño-Fernández, J.A.; Vega-Fleitas, E. ChatGPT Challenges Blended Learning Methodologies in Engineering Education: A Case Study in Mathematics. Appl. Sci. 2023, 13, 6039. [Google Scholar] [CrossRef]
- Mohammed, M.; Kumar, N.; Zawiah, M.; Al-Ashwal, F.Y.; Bala, A.A.; Lawal, B.K.; Wada, A.S.; Halboup, A.; Muhammad, S.; Ahmad, R.; et al. Psychometric Properties and Assessment of Knowledge, Attitude, and Practice Towards ChatGPT in Pharmacy Practice and Education: A Study Protocol. J. Racial Ethn. Health Disparities 2024, 11, 2284–2293. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sayeed, M.S.; Fatimah Abdul Razak, S.; Yogarayan, S.; Sneesl, R. Decision-Making Framework for the Utilization of Generative Artificial Intelligence in Education: A Case Study of ChatGPT. IEEE Access 2024, 12, 95368–95389. [Google Scholar] [CrossRef]
- Adams, D.; Chuah, K.M.; Devadason, E.; Azzis, M.S.A. From novice to navigator: Students’ academic help-seeking behaviour, readiness, and perceived usefulness of ChatGPT in learning. Educ. Inf. Technol. 2023, 1–18. [Google Scholar] [CrossRef]
- Roberts, A. Risk, reward, and resilience framework: Integrative policy making in a complex 910 world. J. Int. Econ. Law 2023, 26, 233–265. [Google Scholar] [CrossRef]
- Eggmann, F.; Weiger, R.; Zitzmann, N.; Blatz, M. Implications of large language models such as ChatGPT for dental medicine. J. Esthet. Restor. Dent. 2023, 35, 1098–1102. [Google Scholar] [CrossRef]
- Victor, B.; Kubiak, S.; Angell, B.; Perron, B. Time to Move Beyond the ASWB Licensing Exams: Can Generative Artificial Intelligence Offer a Way Forward for Social Work? Res. Soc. Work Pract. 2023, 33, 511–517. [Google Scholar] [CrossRef]
- Short, C.; Short, J. The artificially intelligent entrepreneur: ChatGPT, prompt engineering, and entrepreneurial rhetoric creation. J. Bus. Ventur. Insights 2023, 19, e00388. [Google Scholar] [CrossRef]
- Haluza, D.; Jungwirth, D. Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3. Systems 2023, 11, 120. [Google Scholar] [CrossRef]
- Béné, C.; Wood, R.G.; Newsham, A.; Davies, M. Resilience: New utopia or new tyranny? Reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. IDS Work. Pap. 2012, 2012, 1–61. [Google Scholar] [CrossRef]
- Halaweh, M. ChatGPT in education: Strategies for responsible implementation. Contemp. Educ. Technol. 2023, 15, ep421. [Google Scholar] [CrossRef]
- Gupta, K.; Bhaskar, P.; Singh, S. Prioritization of factors influencing employee adoption of e-government using the analytic hierarchy process. J. Syst. Inf. Technol. 2017, 19, 116–137. [Google Scholar] [CrossRef]
- Canco, I.; Kruja, D.; Iancu, T. AHP, a reliable method for quality decision making: A case study in business. Sustainability 2021, 13, 13932. [Google Scholar] [CrossRef]
- Felice, F.; Deldoost, M.; Faizollahi, M.; Petrillo, A. Performance measurement model for the supplier selection based on AHP. Int. J. Eng. Bus. Manag. 2015, 7, 17. [Google Scholar] [CrossRef]
- Jurenka, R.; Cagáňová, D.; Špirková, D. Application of AHP method in decision-making process. In Smart Technology Trends in Industrial and Business Management; Springer: Cham, Switzerland, 2019; pp. 3–15. [Google Scholar]
- Singh, R. Prioritizing the factors for coordinated supply chain using analytic hierarchy process (AHP). Meas. Bus. Excell. 2013, 17, 80–97. [Google Scholar] [CrossRef]
- Sneesl, R.; Jusoh, Y.; Jabar, M.; Abdullah, S.; Bukar, U. Factors Affecting the Adoption of IoT-Based Smart Campus: An Investigation Using Analytical Hierarchical Process (AHP). Sustainability 2022, 14, 8359. [Google Scholar] [CrossRef]
- Sharma, M.; Gupta, R.; Acharya, P. Prioritizing the critical factors of cloud computing adoption using multi-criteria decision-making techniques. Glob. Bus. Rev. 2020, 21, 142–161. [Google Scholar] [CrossRef]
- Saaty, T. The Analytic Hierarchy Process; McGraw-Hill International: New York, NY, USA, 1980. [Google Scholar]
- Douligeris, C.; Pereira, I. A telecommunications quality study using the analytic hierarchy process. IEEE J. Sel. Areas Commun. 1994, 12, 241–250. [Google Scholar] [CrossRef]
- Abduh, M.; Omar, M.A. Islamic-bank selection criteria in Malaysia: An AHP approach. Bus. Intell. J. 2012, 5, 271–281. [Google Scholar]
- Melillo, P.; Pecchia, L. What is the appropriate sample size to run analytic hierarchy process in a survey-based research. In Proceedings of the International Symposium on the Analytic Hierarchy Process, London, UK, 4–7 August 2016; pp. 4–8. [Google Scholar]
- Şahin, M.; Yurdugül, H. A content analysis study on the use of analytic hierarchy process in educational studies. J. Meas. Eval. Educ. Psychol. 2018, 9, 376–392. [Google Scholar] [CrossRef]
- Darko, A.; Chan, A.P.C.; Ameyaw, E.E.; Owusu, E.K.; Pärn, E.; Edwards, D.J. Review of application of analytic hierarchy process (AHP) in construction. Int. J. Constr. Manag. 2019, 19, 436–452. [Google Scholar] [CrossRef]
- Raišienė, A.G.; Raišys, S.J. Business customer satisfaction with B2B consulting services: AHP-based criteria for a new perspective. Sustainability 2022, 14, 7437. [Google Scholar] [CrossRef]
- Dias Jr, A.; Ioannou, P.G. Company and project evaluation model for privately promoted infrastructure projects. J. Constr. Eng. Manag. 1996, 122, 71–82. [Google Scholar] [CrossRef]
- Duke, J.M.; Aull-Hyde, R. Identifying public preferences for land preservation using the analytic hierarchy process. Ecol. Econ. 2002, 42, 131–145. [Google Scholar] [CrossRef]
- Shrestha, R.K.; Alavalapati, J.R.; Kalmbacher, R.S. Exploring the potential for silvopasture adoption in south-central Florida: An application of SWOT–AHP method. Agric. Syst. 2004, 81, 185–199. [Google Scholar] [CrossRef]
- Saaty, T. Decision making, scaling, and number crunching. Decis. Sci. 1989, 20, 404–409. [Google Scholar] [CrossRef]
- Forman, E.; Peniwati, K. Aggregating individual judgments and priorities with the analytic hierarchy process. Eur. J. Oper. Res. 1998, 108, 165–169. [Google Scholar] [CrossRef]
- Gorichanaz, T. Accused: How students respond to allegations of using ChatGPT on assessments. Learn. Res. Pract. 2023, 9, 183–196. [Google Scholar] [CrossRef]
- Yang, S.; Dong, Y.; Yu, Z.G. ChatGPT in Education: Ethical Considerations and Sentiment Analysis. Int. J. Inf. Commun. Technol. Educ. 2024, 20, 1–19. [Google Scholar] [CrossRef]
- Naing, S.Z.S.; Udomwong, P. Public Opinions on ChatGPT: An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis. Data Intell. 2024, 6, 344–374. [Google Scholar] [CrossRef]
- Mamo, Y.; Crompton, H.; Burke, D.; Nickel, C. Higher Education Faculty Perceptions of ChatGPT and the Influencing Factors: A Sentiment Analysis of X. TechTrends 2024, 68, 520–534. [Google Scholar] [CrossRef]
- Rejeb, A.; Rejeb, K.; Appolloni, A.; Treiblmaier, H.; Iranmanesh, M. Exploring the impact of ChatGPT on education: A web mining and machine learning approach. Int. J. Manag. Educ. 2024, 22, 100932. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sayeed, M.S.; Razak, S.F.A.; Yogarayan, S.; Amodu, O.A.; Raja Mahmood, R.A. Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation. SN Comput. Sci. 2024, 5, 366. [Google Scholar] [CrossRef]
- Gammoh, L.A. ChatGPT in academia: Exploring university students’ risks, misuses, and challenges in Jordan. J. Furth. High. Educ. 2024, 48, 608–624. [Google Scholar] [CrossRef]
- Murtiningsih, S.; Sujito, A.; Soe, K.K. Challenges of using ChatGPT in education: A digital pedagogy analysis. Int. J. Eval. Res. Educ. 2024, 13, 3466–3473. [Google Scholar] [CrossRef]
- Reddy, M.R.; Walter, N.G.; Sevryugina, Y.V. Implementation and Evaluation of a ChatGPT-Assisted Special Topics Writing Assignment in Biochemistry. J. Chem. Educ. 2024, 101, 2740–2748. [Google Scholar] [CrossRef]
- Moorhouse, B.L. Beginning and first-year language teachers’ readiness for the generative AI age. Comput. Educ. Artif. Intell. 2024, 6, 100201. [Google Scholar] [CrossRef]
- Ogugua, D.; Yoon, S.N.; Lee, D. Academic Integrity in a Digital Era: Should the Use of ChatGPT Be Banned in Schools? Glob. Bus. Financ. Rev. 2023, 28, 1–10. [Google Scholar]
- Hair, J.; Sarstedt, M.; Ringle, C.; Mena, J. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
- Hair, J.; Risher, J.; Sarstedt, M.; Ringle, C. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook; Springer Nature: Singapore, 2021. [Google Scholar]
- Sohaib, O.; Hussain, W.; Asif, M.; Ahmad, M.; Mazzara, M. A PLS-SEM neural network approach for understanding cryptocurrency adoption. IEEE Access 2019, 8, 13138–13150. [Google Scholar] [CrossRef]
- Sarstedt, M.; Liu, Y. Advanced marketing analytics using partial least squares structural equation modeling (PLS-SEM). J. Mark. Anal. 2023, 12, 1–5. [Google Scholar] [CrossRef]
- Bukar, U.A.; Sidi, F.; Jabar, M.A.; Nor, R.N.H.B.; Abdullah, S.; Ishak, I. A Multistage Analysis of Predicting Public Resilience of Impactful Social Media Crisis Communication in Flooding Emergencies. IEEE Access 2022, 10, 57266–57282. [Google Scholar] [CrossRef]
- Sneesl, R.; Jusoh, Y.Y.; Jabar, M.A.; Abdullah, S. Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network. IEEE Access 2023, 11, 125995–126026. [Google Scholar] [CrossRef]
S/N | Code | Risk Themes | Frequency Count | Related Themes |
---|---|---|---|---|
1 | RIS1 | Infodemics and misinformation | 18 | Quality of output, inaccuracy, nonsense content, Data not apparently updated; limited knowledge, lack of originality |
2 | RIS2 | Bias response | 08 | |
3 | RIS3 | Plagiarism | 08 | |
4 | RIS4 | Privacy and confidentiality | 07 | Data confidentiality |
5 | RIS5 | Academic integrity concern | 07 | |
6 | RIS6 | Risk hallucination through manipulation and mislead | 07 | Deception |
7 | RIS7 | Safety and security concern | 10 | Cybersecurity concerns |
S/N | Code | Reward Themes | Frequency Count | Related Themes |
---|---|---|---|---|
1 | REW1 | Question answering | 10 | Provide feedback, prompt writing, collaboration and friendship, and increased student engagement |
2 | REW2 | Dissemination and diffusion of new information | 08 | Data processing, Data identification, code writing, search engines |
3 | REW3 | Streamlining the workflow | 06 | Documentation |
4 | REW4 | Personalized learning | 08 | Improved literacy, Critical thinking and problem-based learning |
5 | REW5 | Decrease teaching workload | 05 | Teaching and mentoring, support professional activities |
6 | REW6 | Idea and text generation and summarization | 18 | Assemble or organize text, writing fluency and efficiency, hypothesis generation, code writing |
7 | REW7 | Increase productivity and efficiency | 05 | Usefulness |
S/N | Code | Resilience Themes | Frequency Count | Related Themes |
---|---|---|---|---|
1 | RES1 | Appropriate testing framework | 06 | Use AI detector tools |
2 | RES2 | Acceptable usage in science | 05 | |
3 | RES3 | Co-creation between humans and AI | 08 | Improved human-AI interaction, balance between AI-assisted innovation and human expertise, |
4 | RES4 | Academic integrity policies | 10 | Rigorous guidelines; developing policies and procedures |
5 | RES5 | Solidify ethical values | 09 | |
6 | RES6 | Transform educative systems | 11 | Establishment of corresponding pedagogical adjustments, reintroduce proctored, in-person assessments |
7 | RES7 | Higher-level reasoning skills | 11 | Significant training and upskilling |
Score | Meaning | Explanation in This Study |
---|---|---|
1 | Equal | Two themes are equally important |
2 | Weakly important | One theme is weakly more important than the other |
3 | Moderately important | One theme is slightly preferred over the other |
4 | Moderate plus | One theme is moderately more important than the other |
5 | Strongly important | One theme is strongly preferred over the other |
6 | Strong plus | One theme is stronger than the other |
7 | Very strong | One theme is very strongly preferred over the other |
8 | Very, very strong | One theme is much, much stronger than the other |
9 | Absolutely important | One theme is absolutely more important than the other |
N | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.58 | 1.56 |
RRR Elements | Risk | Reward | Resilience | Weights | AW | Lambda | Consistency Test |
---|---|---|---|---|---|---|---|
Risk | 0.33 | 0.32 | 0.34 | 0.3279 | 0.984057 | 3.00098 | |
Reward | 0.22 | 0.21 | 0.21 | 0.2132 | 0.639722 | 3.00064 | ; |
Resilience | 0.45 | 0.47 | 0.46 | 0.4589 | 1.377321 | 3.00139 | ; |
Risk | RIS1 | RIS2 | RIS3 | RIS4 | RIS5 | RIS6 | RIS7 | Weights | Consistency Test |
---|---|---|---|---|---|---|---|---|---|
RIS1 | 0.090 | 0.103 | 0.115 | 0.079 | 0.083 | 0.105 | 0.088 | 0.095 | ; ; ; |
RIS2 | 0.060 | 0.069 | 0.073 | 0.071 | 0.080 | 0.054 | 0.066 | 0.068 | |
RIS3 | 0.104 | 0.125 | 0.132 | 0.145 | 0.124 | 0.109 | 0.158 | 0.128 | |
RIS4 | 0.268 | 0.227 | 0.213 | 0.234 | 0.224 | 0.249 | 0.236 | 0.236 | |
RIS5 | 0.192 | 0.152 | 0.189 | 0.186 | 0.178 | 0.156 | 0.172 | 0.175 | |
RIS6 | 0.074 | 0.110 | 0.105 | 0.082 | 0.099 | 0.087 | 0.075 | 0.090 | |
RIS7 | 0.211 | 0.214 | 0.172 | 0.204 | 0.212 | 0.239 | 0.206 | 0.208 |
Reward | REW1 | REW2 | REW3 | REW4 | REW5 | REW6 | REW7 | Weights | Consistency Test |
---|---|---|---|---|---|---|---|---|---|
REW1 | 0.084 | 0.063 | 0.073 | 0.078 | 0.087 | 0.084 | 0.096 | 0.081 | ; ; ; |
REW2 | 0.110 | 0.082 | 0.066 | 0.071 | 0.061 | 0.089 | 0.094 | 0.082 | |
REW3 | 0.095 | 0.101 | 0.082 | 0.071 | 0.069 | 0.069 | 0.093 | 0.083 | |
REW4 | 0.140 | 0.150 | 0.148 | 0.130 | 0.128 | 0.126 | 0.118 | 0.134 | |
REW5 | 0.132 | 0.183 | 0.164 | 0.139 | 0.137 | 0.095 | 0.151 | 0.143 | |
REW6 | 0.166 | 0.151 | 0.195 | 0.170 | 0.237 | 0.165 | 0.138 | 0.175 | |
REW7 | 0.273 | 0.270 | 0.272 | 0.342 | 0.281 | 0.371 | 0.310 | 0.303 |
Resilience | RES1 | RES2 | RES3 | RES4 | RES5 | RES6 | RES7 | Weights | Consistency Test |
---|---|---|---|---|---|---|---|---|---|
RES1 | 0.062 | 0.062 | 0.048 | 0.059 | 0.055 | 0.077 | 0.078 | 0.063 | ; ; ; |
RES2 | 0.118 | 0.119 | 0.099 | 0.126 | 0.097 | 0.153 | 0.133 | 0.121 | |
RES3 | 0.144 | 0.132 | 0.110 | 0.098 | 0.099 | 0.100 | 0.132 | 0.116 | |
RES4 | 0.197 | 0.177 | 0.212 | 0.187 | 0.199 | 0.095 | 0.188 | 0.179 | |
RES5 | 0.207 | 0.224 | 0.204 | 0.173 | 0.183 | 0.178 | 0.165 | 0.191 | |
RES6 | 0.125 | 0.120 | 0.171 | 0.173 | 0.160 | 0.155 | 0.119 | 0.146 | |
RES7 | 0.147 | 0.166 | 0.156 | 0.185 | 0.207 | 0.243 | 0.186 | 0.184 |
Themes | RRR Element | Global Weight | Rank |
---|---|---|---|
Solidify ethical values | Resilience | 0.08743 | 1 |
Higher-level reasoning skills | Resilience | 0.08458 | 2 |
Academic integrity policies | Resilience | 0.08225 | 3 |
Privacy and confidentiality | Risk | 0.07736 | 4 |
Safety and security concern | Risk | 0.06834 | 5 |
Transform educative systems | Resilience | 0.06697 | 6 |
Increase productivity and efficiency | Reward | 0.06453 | 7 |
Academic integrity concern | Risk | 0.05739 | 8 |
Acceptable usage in science | Resilience | 0.05537 | 9 |
Co-creation between humans and AI | Resilience | 0.05341 | 10 |
Plagiarism | Risk | 0.04202 | 11 |
Idea and text generation and summarization | Reward | 0.03721 | 12 |
Infodemics and misinformation | Risk | 0.03105 | 13 |
Decrease teaching workload | Reward | 0.03051 | 14 |
Risk hallucination through manipulation and misleading | Risk | 0.02957 | 15 |
Appropriate testing framework | Resilience | 0.02888 | 16 |
Personalized learning | Reward | 0.02862 | 17 |
Biased responses | Risk | 0.02220 | 18 |
Streamlining the workflow | Reward | 0.01766 | 19 |
Dissemination and diffusion of new information | Reward | 0.01746 | 20 |
Question answering | Reward | 0.01721 | 21 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bukar, U.A.; Sayeed, M.S.; Razak, S.F.A.; Yogarayan, S.; Sneesl, R. Prioritizing Ethical Conundrums in the Utilization of ChatGPT in Education through an Analytical Hierarchical Approach. Educ. Sci. 2024, 14, 959. https://doi.org/10.3390/educsci14090959
Bukar UA, Sayeed MS, Razak SFA, Yogarayan S, Sneesl R. Prioritizing Ethical Conundrums in the Utilization of ChatGPT in Education through an Analytical Hierarchical Approach. Education Sciences. 2024; 14(9):959. https://doi.org/10.3390/educsci14090959
Chicago/Turabian StyleBukar, Umar Ali, Md Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, and Radhwan Sneesl. 2024. "Prioritizing Ethical Conundrums in the Utilization of ChatGPT in Education through an Analytical Hierarchical Approach" Education Sciences 14, no. 9: 959. https://doi.org/10.3390/educsci14090959