Employing Artificial Intelligence Applications to Evaluate Faculty Development Programs by Kirkpatrick's Model
(Case Study: University of Tabuk)
الكلمات المفتاحية:
Artificial Intelligence Applications, Faculty Development Program, Kirkpatrick's Model
الملخص
Objectives: This study aims to explore the level employing artificial intelligence (AI) applications to evaluate faculty's reaction, learning process, behavior change process and the impact of the faculty development programs at University of Tabuk by Kirkpatrick's Model.
Methodology: The researcher used the descriptive method and developed a questionnaire which was distributed to a study sample of (492) faculty members working at University of Tabuk in Saudi Arabia in 2024.
Results: The results showed that the effectiveness of faculty development programs based on Kirkpatrick's Model, with varying outcomes across its evaluation levels. The Impact of the Training Program on the Organization ranks highest (Mean = 2.4501, SD = 0.59333) with a "High Possibility" rating, suggesting strong perceived organizational benefits. Reactions (Mean = 2.3963, SD = 0.59170) and Learning Process (Mean = 2.3670, SD = 0.62103) also fall into the "High Possibility" category, reflecting positive participant feedback and effective knowledge acquisition. However, Behavior Change (Mean = 2.2894, SD = 0.66252) is rated as "Medium Possibility," indicating challenges in translating learning into sustained workplace behavior.
Conclusions: The findings suggest that AI provides a powerful tool for overcoming the limitations of traditional evaluation methods, offering more precise, detailed, and dynamic insights into the effectiveness of professional development initiatives. By addressing the challenges associated with data quality, algorithmic bias, and ethical considerations, University of Tabuk can harness the full potential of AI to enhance their faculty development programs, ultimately contributing to the broader objectives of educational excellence and national development outlined in Vision 2030.
المراجع
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11. AlSayiryah, M. B. A. (2025). Developing community partnerships in higher education institutions in the Sultanate of Oman in light of the age of artificial intelligence. The Jordanian Journal of Educational Sciences, 20(3), 595–608. Retrieved from https://jjes.yu.edu.jo/index.php/jjes/article/view/1104
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13. Alzayed, Z., & Hajj Omar, S. S. (2016). The Influence of Online Professional Learning Communities on Science Teachers' Understanding of Nature of Science and itsTeaching Practices. The Jordanian Journal of Educational Sciences, 12(3), 349–362. Retrieved from: https://jjes.yu.edu.jo/index.php/jjes/article/view/661
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23. Chiu, T. K., Meng, H., Chai, C. S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30-39.
24. Ding, A. C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178
25. Duan, H. & Zhao, W. (2024). The Effects of Educational Artificial Intelligence-Powered Applications on Teachers’ Perceived Autonomy, Professional Development for Online Teaching, and Digital Burnout. International Review of Research in Open and Distributed Learning, 25(3), 57–76. https://doi.org/10.19173/irrodl.v25i3.7659
26. Eddy, S. L., & Hogan, K. A. (2016). Getting under the hood: How and for whom does increasing course structure work? CBE—Life Sciences Education, 15(3), ar43.
27. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.
28. Hattie, J., & Marsh, H. W. (1996). The relationship between research and teaching: A meta-analysis. Review of Educational Research, 66(4), 507–542.
29. Higgins, R. C. (2016). Analysis for financial management (11th ed.). McGraw-Hill Education.
30. Hodge, S. R., & Preston, C. (2004). Faculty development: A critical link in improving retention and student success. Innovative Higher Education, 29(2), 161–174.
31. Joo, B. K., & Lim, S. (2019). Effects of training evaluation on training effectiveness: A meta-analysis. Human Resource Development Review, 18(4), 447–468.
32. Katsamakas, E., Pavlov, O. V., & Saklad, R. (2024). Artificial intelligence and the transformation of higher education institutions: A systems approach. Sustainability, 16(14), 6118. https://doi.org/10.3390/su16146118
33. Kember, D., Ho, A., & Hong, C. (2010). The importance of establishing relevance in motivating student learning. Active Learning in Higher Education, 11(3), 167–177.
34. Khan, M., & Baig, M. (2023). Artificial intelligence in higher education: Applications and implications for faculty development. Computers & Education: Artificial Intelligence, 4, 100119.
35. Khairi, F., & Alhafidh, F. (2024). Leveraging AI for enhanced teaching efficiency and effectiveness. https://doi.org/10.13140/RG.2.2.27462.08007.
36. Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels (3rd ed.). Berrett-Koehler Publishers.
37. Kumar, A., & Shah, N. (2020). Artificial intelligence in education: A review. Journal of Educational Technology in Higher Education, 17(1), 1–19.
38. Li, N., & Chen, Y. (2021). Artificial intelligence-assisted training: A systematic review of the literature. Educational Research Review, 34, 100384.
39. Liu, Y., & Jiang, C. (2018). The effectiveness of training evaluation: A systematic review and meta-analysis. Human Resource Development Quarterly, 29(4), 401–429.
40. Mah, P. M., Skalna, I., & Muzam, J. (2022). Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0. Applied Sciences, 12(18), 9207. https://doi.org/10.3390/app12189207.
41. Mah, D. K., & Groß, N. (2024). Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs. International Journal of Educational Technology in Higher Education, 21(1), 58.
42. Maqsood, N., Prendeville, P., & Bhatti, S. H. (2024). Measuring the impact of faculty development programmes on business schools’ performance in Pakistan. Journal of Education for Business, 99(5), 312–322. https://doi.org/10.1080/08832323.2024.2372498.
43. Martinez, M., Rigueira, X., Larrañaga, A., Martínez, J., Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review. Revista de Psicodidáctica, 28. https://doi.org/10.1016/j.psicoe.2023.06.002.
44. Holmes, W., & Porayska-Pomsta, K. (2023). The ethics of artificial intelligence in education. Lontoo: Routledge.
45. Randhawa, G. K., & Jackson, M. (2020). The role of artificial intelligence in learning and professional development for healthcare professionals. Healthcare management forum, 33(1), 19–24. https://doi.org/10.1177/0840470419869032
2. Al-Abyadh, M.H.A. (2025). The influence of technostress and gratitude on university students’ vitality: theroles of prosocial behaviours and psychological resilience’, Int. J. Innovationand Learning, Vol. 37, No. 2, pp.149–178. https://doi.org/10.1504/IJIL.2025.144197
3. Alasmrai, M. (2023). The Role of Digital Professional Learning Communities in Enhancing The implementation of Action Research: Approach to Teacher Sustainable Professional Development. New Horizons in Adult Education Journal, 34(34), 1-17. https://doi: 10.21608/afbj.2023.347058
4. Alenezi, A. (2021). Innovative teaching strategies and technology integration in Saudi higher education: A case study of the University of Tabuk. International Journal of Educational Technology in Higher Education. 18(1). https://doi.org/10.1186/s41239-021-00259-7
5. Alenezi, H. S., & Faisal, M. H. (2020). Utilizing crowdsourcing and machine learning in education: Literature review. Education and Information Technologies, 25(4), 2971–2986. https://doi.org/10.1007/s10639-020-10102-w
6. AlGhamdi, A. (2022). Using AI to predict faculty performance: A study of Saudi universities. Journal of Artificial Intelligence in Education, 12(4), 233–250.
7. AlGhamdi, A., & Al-Faifi, A. (2022). The impact of faculty development programs on teaching effectiveness in Saudi universities. Journal of Higher Education Research, 15(4), 243–258.
8. Alharbi, F. (2021). Assessing the effectiveness of faculty development programs in Saudi Arabia: A multi-case study approach. Middle Eastern Journal of Education, 7(2), 52–65.
9. Alqahtani, M., & Rajab, H. (2020). Faculty training and development: Enhancing quality of education in Saudi Arabian universities. Journal of Education and Practice, 11(24), 1–8.
10. Alqarni, A. (2023). Measuring the relative efficiency of the colleges at the University of Tabuk using Data Envelopment Analysis (DEA) for the academic year 2020. The Jordanian Journal of Educational Sciences, 19(1), 61–82. https://doi.org/10.47015/19.1.4
11. AlSayiryah, M. B. A. (2025). Developing community partnerships in higher education institutions in the Sultanate of Oman in light of the age of artificial intelligence. The Jordanian Journal of Educational Sciences, 20(3), 595–608. Retrieved from https://jjes.yu.edu.jo/index.php/jjes/article/view/1104
12. Alsufyani, H., & Alnajdi, S. (2023). Degree of employees' readiness at Prince Nayef Bin Abdul-Aziz Academy of Drugs Control to employ artificial intelligence (AI) in training. Management Studies Journal, 89(1), 1902–1965. https://doi.org/10.21608/mkmgt.2023.204897.1532
13. Alzayed, Z., & Hajj Omar, S. S. (2016). The Influence of Online Professional Learning Communities on Science Teachers' Understanding of Nature of Science and itsTeaching Practices. The Jordanian Journal of Educational Sciences, 12(3), 349–362. Retrieved from: https://jjes.yu.edu.jo/index.php/jjes/article/view/661
14. Arqawi, S., Zitawi, E., Rabaya, A., Abunasser, B., & Abu-Naser, S. (2022). Predicting university student retention using artificial intelligence. International Journal of Advanced Computer Science and Applications, 13(9), Article 937. https://doi.org/10.14569/IJACSA.2022.0130937
15. Azmy, A., & Setiarini, N. Y. (2023). Kirkpatrick model as evaluation training program for assessor: Case study of government employee. International Journal of Management, Accounting and Economics, 10(9), 675–696. https://zenodo.org/records/10115328
16. Bhatt, P., & Muduli, A. (2023). Artificial Intelligence in Learning and Development: A Systematic Literature Review. European Journal of Training and Development, 47(7/8), 677–694. https://doi:10.1108/ejtd-09-2021-0143
17. Baldwin, T. T., Ford, J. K., & Blume, B. D. (2019). Transfer of training 2018: Current issues and future directions. Journal of Business and Psychology, 34(1), 1–18.
18. Benvenuti, M., Cangelosi, A., Weinberger, A., Mazzoni, E., Benassi, M., Barbaresi, M., & Orsoni, M. (2023). Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Computers in Human Behavior, 148, 107903. https://doi.org/10.1016/j.chb.2023.107903
19. Blanchard, P. N., & Thacker, J. W. (2013). Effective training: Systems, strategies, and practices (5th ed.). Pearson.
20. Brigham, E. F., & Houston, J. F. (2021). Fundamentals of financial management (15th ed.). Cengage Learning.
21. Boudreau, K. J., & Ramstad, P. M. (2020). AI in training and development. MIT Sloan Management Review, 61(2), 59–67.
22. Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16-24. https://doi.org/10.1016/j.procs.2018.08.233
23. Chiu, T. K., Meng, H., Chai, C. S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30-39.
24. Ding, A. C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178
25. Duan, H. & Zhao, W. (2024). The Effects of Educational Artificial Intelligence-Powered Applications on Teachers’ Perceived Autonomy, Professional Development for Online Teaching, and Digital Burnout. International Review of Research in Open and Distributed Learning, 25(3), 57–76. https://doi.org/10.19173/irrodl.v25i3.7659
26. Eddy, S. L., & Hogan, K. A. (2016). Getting under the hood: How and for whom does increasing course structure work? CBE—Life Sciences Education, 15(3), ar43.
27. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.
28. Hattie, J., & Marsh, H. W. (1996). The relationship between research and teaching: A meta-analysis. Review of Educational Research, 66(4), 507–542.
29. Higgins, R. C. (2016). Analysis for financial management (11th ed.). McGraw-Hill Education.
30. Hodge, S. R., & Preston, C. (2004). Faculty development: A critical link in improving retention and student success. Innovative Higher Education, 29(2), 161–174.
31. Joo, B. K., & Lim, S. (2019). Effects of training evaluation on training effectiveness: A meta-analysis. Human Resource Development Review, 18(4), 447–468.
32. Katsamakas, E., Pavlov, O. V., & Saklad, R. (2024). Artificial intelligence and the transformation of higher education institutions: A systems approach. Sustainability, 16(14), 6118. https://doi.org/10.3390/su16146118
33. Kember, D., Ho, A., & Hong, C. (2010). The importance of establishing relevance in motivating student learning. Active Learning in Higher Education, 11(3), 167–177.
34. Khan, M., & Baig, M. (2023). Artificial intelligence in higher education: Applications and implications for faculty development. Computers & Education: Artificial Intelligence, 4, 100119.
35. Khairi, F., & Alhafidh, F. (2024). Leveraging AI for enhanced teaching efficiency and effectiveness. https://doi.org/10.13140/RG.2.2.27462.08007.
36. Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels (3rd ed.). Berrett-Koehler Publishers.
37. Kumar, A., & Shah, N. (2020). Artificial intelligence in education: A review. Journal of Educational Technology in Higher Education, 17(1), 1–19.
38. Li, N., & Chen, Y. (2021). Artificial intelligence-assisted training: A systematic review of the literature. Educational Research Review, 34, 100384.
39. Liu, Y., & Jiang, C. (2018). The effectiveness of training evaluation: A systematic review and meta-analysis. Human Resource Development Quarterly, 29(4), 401–429.
40. Mah, P. M., Skalna, I., & Muzam, J. (2022). Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0. Applied Sciences, 12(18), 9207. https://doi.org/10.3390/app12189207.
41. Mah, D. K., & Groß, N. (2024). Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs. International Journal of Educational Technology in Higher Education, 21(1), 58.
42. Maqsood, N., Prendeville, P., & Bhatti, S. H. (2024). Measuring the impact of faculty development programmes on business schools’ performance in Pakistan. Journal of Education for Business, 99(5), 312–322. https://doi.org/10.1080/08832323.2024.2372498.
43. Martinez, M., Rigueira, X., Larrañaga, A., Martínez, J., Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review. Revista de Psicodidáctica, 28. https://doi.org/10.1016/j.psicoe.2023.06.002.
44. Holmes, W., & Porayska-Pomsta, K. (2023). The ethics of artificial intelligence in education. Lontoo: Routledge.
45. Randhawa, G. K., & Jackson, M. (2020). The role of artificial intelligence in learning and professional development for healthcare professionals. Healthcare management forum, 33(1), 19–24. https://doi.org/10.1177/0840470419869032
منشور
2025-03-23
كيفية الاقتباس
Dr. Mohammed Awad Alasmrai. (2025). Employing Artificial Intelligence Applications to Evaluate Faculty Development Programs by Kirkpatrick’s Model: (Case Study: University of Tabuk). Journal of Arts, Literature, Humanities and Social Sciences, (118), 393-424. https://doi.org/10.33193/JALHSS.118.2025.1387
إصدار
القسم
المقالات
الحقوق الفكرية (c) 2025 Dr. Mohammed Awad Alasmrai
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