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Towards Reliable Utilization: An Instructional Design Model for Integrating Generative Pre-trained Transformer (GPT) in Education

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Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom

Part of the book series: Studies in Big Data ((SBD,volume 144))

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Abstract

The rapid progress in artificial intelligence has brought in innovative opportunities for enhancing education through the integration of Generative Pre-Trained Transformer (GPT) technology. This research presents a comprehensive model, the Generative Pre-Trained Transformer Instructional Design (GPTID) model, designed to seamlessly integrate GPT capabilities into educational programs. The model's development encompasses a meticulous three-stage process, consisting of prototype creation, expert validation, and refinement. The GPTID model encompasses seven phases, each highlighting specific steps and considerations for educators. The phases include Needs Analysis, GPT Tools Planning, Ethical Framework, Prompt Engineering, Implementation, Evaluation, and Recommendations. Throughout this study, an emphasis is placed on ethical considerations, user guidance, and systematic evaluation, ensuring the responsible and productive utilization of GPT technology in educational settings. Future research recommendations focus on the evaluation of the model's effectiveness across various educational settings, promoting continuous refinement and optimization for enhanced learning outcomes. This research provides a valuable framework for educators and instructional designers to utilize the potential of GPT technology to enhance educational practices, foster engagement, and promote responsible AI integration in education.

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References

  1. A.M. Alfaisal, A. Zare, A. Alshaafi, R. Aljanada, R. M. Alfaisal, G.W. Abukhalil, “Predicting the actual use of social media sites among university communicators: using PLS-SEM and ML approaches”

    Google Scholar 

  2. R. Aljanada, G.W. Abukhalil, A. M. Alfaisal, R.M. Alfaisal, “Adoption of Google Glass technology: PLS-SEM and machine learning analysis”

    Google Scholar 

  3. R. Alfaisal et al., “Predicting the intention to use google glass in the educational projects: A hybrid SEM-ML approach”

    Google Scholar 

  4. K. Alhumaid, N. Alnazzawi, I. Akour, O. Khasoneh, R. Alfaisal, S. Salloum, An integrated model for the usage and acceptance of stickers in WhatsApp through SEM-ANN approach. Int. J. Data Netw. Sci. 6(4), 1261–1272 (2022)

    Article  Google Scholar 

  5. A. Aburayya et al., SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE. Int. J. Data Netw. Sci. 7(2), 667–676 (2023)

    Article  Google Scholar 

  6. S. Salloum et al., Sustainability model for the continuous intention to use metaverse technology in higher education: A case study from Oman. Sustainability 15(6), 5257 (2023)

    Article  Google Scholar 

  7. A.W. Alawadhi M, K. Alhumaid, S. Almarzooqi, Sh. Aljasmi, A. Aburayya, S.A. Salloum, “Factors affecting medical students’ acceptance of the metaverse system in medical training in the United Arab Emirates.” SEEJPH 5 (2022)

    Google Scholar 

  8. S.A. Salloum et al., “Novel machine learning based approach for analysing the adoption of metaverse in medical training: A UAE case study.” Inform. Med. Unlocked, 101354 (2023)

    Google Scholar 

  9. K. Alhumaid et al., “Predicting the intention to use audi and video teaching styles: An empirical study with PLS-SEM and machine learning models.” in International Conference on Advanced Machine Learning Technologies and Applications (2022), pp. 250–264

    Google Scholar 

  10. M. Elareshi, M. Habes, E. Youssef, S.A. Salloum, R. Alfaisal, A. Ziani, “SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid.” Heliyon, e09236 (2022)

    Google Scholar 

  11. K. Seo, J. Tang, I. Roll, S. Fels, D. Yoon, The impact of artificial intelligence on learner–instructor interaction in online learning. Int. J. Educ. Technol. High. Educ. 18(1), 1–23 (2021)

    Article  Google Scholar 

  12. R.M. Alfaisal, A. Zare, A.M. Alfaisal, R. Aljanada, G.W. Abukhalil, The acceptance of metaverse system: a hybrid SEM-ML approach. Int. J. Adv. Appl. Comput. Intell. 1(1), 34–44 (2022)

    Google Scholar 

  13. R. Alfaisal, H. Hashim, U.H. Azizan, “Metaverse system adoption in education: A systematic literature review” J. Comput. Educ., 1–45 (2022)

    Google Scholar 

  14. I. Akour, N. Alnazzawi, R. Alfaisal, S.A. Salloum, “Using classical machine learning for phishing websites detection from URLS”

    Google Scholar 

  15. M.A. Almaiah et al., Examining the impact of artificial intelligence and social and computer anxiety in E-learning settings: Students’ perceptions at the University level. Electronics 11(22), 3662 (2022)

    Article  Google Scholar 

  16. M.A. Almaiah et al., Determinants influencing the continuous intention to use digital technologies in Higher Education. Electronics 11(18), 2827 (2022)

    Article  Google Scholar 

  17. R. Al-Maroof et al., Students’ perception towards using electronic feedback after the pandemic: Post-acceptance study. Int. J. Data Netw. Sci. 6(4), 1233–1248 (2022)

    Article  Google Scholar 

  18. R.S. Al-Maroof et al., “The effectiveness of online platforms after the pandemic: Will face-to-face classes affect students’ perception of their Behavioural Intention (BIU) to use online platforms?.” Informatics 8(4), 83 (2021)

    Google Scholar 

  19. U.S. Department of Education, “Reimagining the role of technology in education” (2017)

    Google Scholar 

  20. “OpenAI,” ChatGPT (Sep 25 version) [Large language model], (2023) [Online]. Available: https://chat.openai.com/chat

  21. A. Abramson, “How to use ChatGPT as a learning tool.” Am. Psychol. Assoc. (2023)

    Google Scholar 

  22. H. Abuhassna, S. Alnawajha, Instructional design made easy! instructional design models, categories, frameworks, educational context, and recommendations for future work. Eur. J. Investig. Heal. Psychol. Educ. 13(4), 715–735 (2023)

    Google Scholar 

  23. F. Shwedeh et al., SMEs’ innovativeness and technology adoption as downsizing strategies during COVID-19: The moderating role of financial sustainability in the tourism industry using structural equation modelling. Sustainability 14(23), 16044 (2022)

    Article  Google Scholar 

  24. K. Tahat et al., Detecting fake news during the COVID-19 pandemic: A SEM-ML approach. Comput. Integr. Manuf. Syst. 28(12), 1554–1571 (2022)

    Google Scholar 

  25. M. Habes et al., “Students’ perceptions of mobile learning technology acceptance during Covid-19: WhatsApp in focus.” EMI. Educ. Media Int., 1–19, (2022)

    Google Scholar 

  26. R. Almaiah, M.A. Alhumaid, K. Aldhuhoori, A. Alnazzawi, N. Aburayya, A. Alfaisal, R. Salloum, S.A. Lutfi, A. Al Mulhem, A. Alkhdour, T. Awad, A.B. Shehab, “Factors affecting the adoption of digital information technologies in higher education: an empirical study.” Electronics 11(3572), (2022)

    Google Scholar 

  27. M.A. Almaiah et al., “Integrating Teachers’ TPACK levels and students’ learning motivation, technology innovativeness, and optimism in an iot acceptance model. Electronics 11, 3197 (2022).” s Note: MDPI stays neu-tral with regard to jurisdictional claims in …, 2022

    Google Scholar 

  28. M.A. Almaiah et al., Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics 11(20), 3291 (2022)

    Article  Google Scholar 

  29. R.S. Al-Maroof et al., Students’ perception towards behavioral intention of audio and video teaching styles: An acceptance study. Int. J. Data Netw. Sci. 6(2), 603 (2022)

    Article  Google Scholar 

  30. I. Akour et al., A conceptual model for investigating the effect of privacy concerns on e-commerce adoption: A study on United Arab Emirates consumers. Electronics 11(22), 3648 (2022)

    Article  Google Scholar 

  31. I. Shahin, A.B. Nassif, A. Elnagar, S. Gamal, S.A. Salloum, A. Aburayya, Neurofeedback interventions for speech and language impairment: A systematic review. J. Manag. Inf. Decis. Sci. 24, 1–30 (2021)

    Google Scholar 

  32. D.L. Capuyan et al., Adaptation of innovative edge banding trimmer for technology instruction: A university case. World J. Educ. Technol. Curr. Issues 13(1), 31–41 (2021)

    Article  Google Scholar 

  33. E. Mouzaek, N. Alaali, S.A. Salloum, A. Aburayya, “An empirical investigation of the impact of service quality dimensions on guests satisfaction: A case study of Dubai hotels.” J. Contemp. Issues Bus. Gov. 27(3), 1186–1199 (2021)

    Google Scholar 

  34. I. Makki, N. Rahmani, M. Aljasmi, S. Mubarak, S.A. Salloum11, N. Alaali, “The impact of the COVID-19 pandemic on the mental health status of healthcare providers in the primary health care sector in Dubai” (2020)

    Google Scholar 

  35. T. Gaber, A. Tharwat, V. Snasel, A.E. Hassanien, “Plant identification: Two dimensional-based vs. one dimensional-based feature extraction methods,” in 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (2015), pp. 375–385

    Google Scholar 

  36. N.A. Samee et al., “Metaheuristic optimization through deep learning classification of COVID-19 in chest X-ray images.” Comput. Mater. Contin. 73(2) (2022)

    Google Scholar 

  37. A. Tharwat, T. Gaber, M.M. Fouad, V. Snasel, A.E. Hassanien, Towards an automated zebrafish-based toxicity test model using machine learning. Procedia Comput. Sci. 65, 643–651 (2015)

    Article  Google Scholar 

  38. S. Al-Skaf, E. Youssef, M. Habes, K. Alhumaid, S.A. Salloum, The Acceptance of social media sites: An empirical study using PLS-SEM and ML approaches. Adv. Mach. Learn. Technol. Appl.: Proc. AMLTA 2021, 548–558 (2021)

    Google Scholar 

  39. M. Taryam et al., “Effectiveness of not quarantining passengers after having a negative COVID-19 PCR test at arrival to Dubai airports.” Syst. Rev. Pharm., 1384–1395 (2020)

    Google Scholar 

  40. S. Akgun, C. Greenhow, “Artificial intelligence in education: Addressing ethical challenges in K-12 settings.” AI Ethics, 1–10 (2021)

    Google Scholar 

  41. A. Alsharhan, S. Salloum, A. Aburayya, Technology acceptance drivers for AR smart glasses in the middle east: A quantitative study. Int. J. Data Netw. Sci. 6(1), 193–208 (2022)

    Article  Google Scholar 

  42. A. Aburayya, D. Alawadhi, M. Taryam, A conceptual framework for implementing TQM in the primary healthcare centers and examining its impact on patient satisfaction. Int. J. Adv. Res. 7(3), 1047–1065 (2019)

    Article  Google Scholar 

  43. S.R. AlSuwaidi, M. Alshurideh, B. Al Kurdi, A. Aburayya, “The main catalysts for collaborative R&D projects in Dubai industrial sector.” in The International Conference on Artificial Intelligence and Computer Vision (2021), pp. 795–806

    Google Scholar 

  44. A. Almusaed, A. Almssad, I. Yitmen, R.Z. Homod, Enhancing student engagement: Harnessing ‘AIED’’s power in hybrid education—a review analysis. Educ. Sci. 13(7), 632 (2023)

    Article  Google Scholar 

  45. A. Almarzouqi, A. Aburayya, S.A. Salloum, Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach. PLoS ONE 17(8), e0272735 (2022)

    Article  Google Scholar 

  46. A. Almarzouqi, A. Aburayya, S.A. Salloum, Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Inform. Med. Unlocked 28, 100859 (2022)

    Article  Google Scholar 

  47. A. Aburayya, A. Al Marzouqi, I. Al Ayadeh, A. Albqaeen, S. Mubarak, “Evolving a hybrid appointment system for patient scheduling in primary healthcare centres in Dubai: Perceptions of patients and healthcare provider.” Int. J. Emerg. Technol. 11(2), 251–260 (2020)

    Google Scholar 

  48. R. Luckin, M. Cukurova, Designing educational technologies in the age of AI: A learning sciences-driven approach. Br. J. Educ. Technol. 50(6), 2824–2838 (2019)

    Article  Google Scholar 

  49. J. Kim, H. Lee, Y.H. Cho, Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Educ. Inf. Technol. 27(5), 6069–6104 (2022)

    Article  Google Scholar 

  50. A. Tlili et al., What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 10(1), 15 (2023)

    Article  Google Scholar 

  51. G. Yenduri et al., “Generative pre-trained transformer: a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions.” arXiv Prepr. arXiv2305.10435 (2023)

    Google Scholar 

  52. S.T.H. Pham, P.M. Sampson, The development of artificial intelligence in education: A review in context. J. Comput. Assist. Learn. 38(5), 1408–1421 (2022)

    Article  Google Scholar 

  53. M. Firat, How chat GPT can transform autodidactic experiences and open education? Anadolu Unive, Dep. Distance Educ. Open Educ. Fac, (2023)

    Google Scholar 

  54. D.A. Konstantinova, L.V. Vorozhikhin, V.V. Petrov, A.M. Titova, E.S. Shtykhno, “Generative artificial intelligence in education: discussions and forecasts.” Open Educ. 2(27), 36–48 (2023)

    Google Scholar 

  55. A. Renato, I. Nadia, Francisco, “Challenges and Opportunities of AI-Assisted Learning: A Systematic Literature Review on the Impact of ChatGPT Usage in Higher Education.” Int. J. Learn. Teach. Educ. Res. 22(7), 122–135 (2023). https://doi.org/10.26803/ijlter.22.7.7

  56. J. Su, W. Yang, “Unlocking the power of ChatGPT: A framework for applying generative AI in education.” ECNU Rev. Educ., 20965311231168424 (2023)

    Google Scholar 

  57. E.A. Andreea, Instructional design in education. IJAEDU-Int. E-J. Adv. Educ. 8(24), 219–224 (2023)

    Google Scholar 

  58. E. Yıldız, H. Uzunboylu, Comparıson of instructional design models: An instructional design model; example of the near east unıversıty. Int. J. Innov. Res. Educ. 5(3), 74–84 (2018)

    Article  Google Scholar 

  59. M.K. Khalil, I.A. Elkhider, “Applying learning theories and instructional design models for effective instruction.” Adv. Physiol. Educ. (2016)

    Google Scholar 

  60. S. Kainulainen, “Research and Development (R&D),” Michalos, A.C. Encycl. Qual. Life Well-Being Res. Springer, Dordrecht. (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Professional Postgraduate Diploma in Teaching Department, City University Ajman, Ajman, UAE

    Moustafa Kamal Moussa

  2. Educational Technology Department, Faculty of Specific Education, Ain Shams University, Cairo, Egypt

    Moustafa Kamal Moussa

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  1. Moustafa Kamal Moussa
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Correspondence to Moustafa Kamal Moussa .

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Editors and Affiliations

  1. University of Sharjah, Sharjah, United Arab Emirates

    Amina Al-Marzouqi

  2. University of Sharjah, Sharjah, United Arab Emirates

    Said A. Salloum

  3. University of Sharjah, University City, Sharjah, United Arab Emirates

    Mohammed Al-Saidat

  4. City University Ajman, Ajman, United Arab Emirates

    Ahmed Aburayya

  5. City University Ajman, Ajman, United Arab Emirates

    Babeet Gupta

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Moussa, M.K. (2024). Towards Reliable Utilization: An Instructional Design Model for Integrating Generative Pre-trained Transformer (GPT) in Education. In: Al-Marzouqi, A., Salloum, S.A., Al-Saidat, M., Aburayya, A., Gupta, B. (eds) Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom. Studies in Big Data, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-52280-2_30

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