Ethics and Artificial Intelligence in Education: A Needs Analysis for Developing a Responsible AI Implementation Framework in the Classroom
DOI:
https://doi.org/10.56294/mw2025820Keywords:
Artificial Intelligence in Education, Ethics, Responsible AI, Needs Analysis, Ethical FrameworkAbstract
The fast adoption of Artificial Intelligence (AI) in the education sector has brought great changes in the learning environment by means of intelligent tutoring systems, automated grading, adaptive learning, and learning analytics. Although these innovations improve personalization, efficiency, and inclusivity, these inventions also produce ethical dilemmas of data privacy, bias in algorithms, transparency, accountability, and equity. This paper will examine the needs and perceptions of the stakeholders with a view to coming up with a responsible framework for applying AI ethically in educational institutions. A quantitative strategy was used in a descriptive manner and comprised 100 stakeholders purposely selected; this includes teachers, students, administrators, and developers. The data were gathered using a master structured questionnaire that covered the issues of AI literacy, perceived ethical risks, perceived relevance of ethical principles, and predicted institutional structures. The findings have shown that the participants have a fairly good conceptualization of AI (mean = 3.9) and are highly aware of the risks of ethics and especially on data privacy (4.2) and accountability (4.3). Respondents anticipated high moral values like fairness, inclusiveness, transparency, and safeguarding of personal data, and they stressed the essence of institutional rules, technical guidelines, training, and inter-stakeholder cooperation to guarantee the responsible use of AI. These results demonstrate the pressing need to develop a context-sensitive ethical AI model that can strike a balance between technological innovation and human values, thus encouraging trust, equity, and quality in learning and providing policymakers and practitioners with capacity building, institutional governance, and participatory approaches to responsible AI implementation in learning settings.
References
1. Kong SC, Zhu J. Developing and validating an artificial intelligence ethical awareness scale for secondary and university students: Cultivating ethical awareness through problem-solving with artificial intelligence tools. Computers and Education: Artificial Intelligence. 2025 Dec;9.
2. Tolentino R, Hersson-Edery F, Yaffe M, Abbasgholizadeh-Rahimi S. AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study. JMIR Med Educ. 2025 Apr 25;11.
3. Ayanwale MA, Adelana OP, Molefi RR, Adeeko O, Ishola AM. Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open. 2024 Jun;6.
4. Vetter MA, Lucia B, Jiang J, Othman M. Towards a framework for local interrogation of AI ethics: A case study on text generators, academic integrity, and composing with ChatGPT. Comput Compos. 2024 Mar;71:102831.
5. Huyen NT. Fostering Design Thinking mindset for university students with NPCs in the metaverse. Heliyon. 2024 Aug;10(15):e34964.
6. Li M, Armstrong SJ. The relationship between Kolb’s experiential learning styles and Big Five personality traits in international managers. Pers Individ Dif. 2015 Nov;86:422–6.
7. Saifi AG, Khlaif ZN, Affouneh S. The effect of using community-based learning program in science students’ achievement according to Kolb’s learning styles. Social Sciences & Humanities Open. 2024;10:101125.
8. Russell RG, White J, Karns A, Rodriguez K, Jeffries PR, Sengstack P. Toward amplifying the good in nursing education: A quality improvement study on implementing artificial intelligence-based assistants in a learning system. Nurs Outlook. 2025 Sep;73(5):102483.
9. Donkor A, Boakye E, Atuanor P, Wiafe YA. Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A pre-test/post-test study design. Radiography. 2025 Jul;31(4):102987.
10. Boulhrir T, Hamash M. Unpacking artificial intelligence in elementary education: A comprehensive thematic analysis systematic review. Computers and Education: Artificial Intelligence. 2025 Dec;9:100442.
11. Wu D, Chen M, Chen X, Liu X. Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy. Computers and Education: Artificial Intelligence. 2024 Dec;7:100295.
12. Salido A, Syarif I, Sitepu MS, Suparjan, Wana PR, Taufika R, et al. Integrating critical thinking and artificial intelligence in higher education: A bibliometric and systematic review of skills and strategies. Social Sciences & Humanities Open. 2025;12:101924.
13. Kong SC, Hu W. A study of developing administrative Staff’s conceptual understanding of generative artificial intelligence through professional Development: Evaluation of a course using tests, surveys and thematic analysis of reflective writings. Computers and Education: Artificial Intelligence. 2025 Dec;9:100444.
14. Ukwandu E, Omisade O, Jones K, Thorne S, Castle M. The future of teaching and learning in the context of emerging artificial intelligence technologies. Futures. 2025 Aug;171:103616.
15. Ronksley-Pavia M, Nguyen L, Wheeley E, Rose J, Neumann MM, Bigum C, et al. A scoping literature review of generative artificial intelligence for supporting neurodivergent school students. Computers and Education: Artificial Intelligence. 2025 Dec;9:100437.
16. Simms RC. Generative artificial intelligence (AI) literacy in nursing education: A crucial call to action. Nurse Educ Today. 2025 Mar;146:106544.
17. Kong SC, Hu W. A study of developing administrative Staff’s conceptual understanding of generative artificial intelligence through professional Development: Evaluation of a course using tests, surveys and thematic analysis of reflective writings. Computers and Education: Artificial Intelligence. 2025 Dec;9:100444.
18. Veldhuis A, Lo PY, Kenny S, Antle AN. Critical Artificial Intelligence literacy: A scoping review and framework synthesis. Int J Child Comput Interact. 2025 Mar;43:100708.
19. Jalinus N, Nabawi RA. The instructional media development of welding practice course based on PjBL model : enhancing student engagement and student competences. International Journal Innovation and Learning. 2018;24(4).
20. Jalinus N, Nabawi RA, Mardin A. The Seven Steps of Project Based Learning Model to Enhance Productive Competences of Vocational Students. 2017;102(Ictvt):251–6.
21. Jalinus N, Ganefri G, Syahril S, Zaus MA, Islami S. Gamification in work-based learning in vocational education to support students’ coding abilities. Indonesian Journal of Electrical Engineering and Computer Science. 2025 Feb 1;37(2):1262.
22. Sun J, Oubibi M, Hryshayeva K. Exploring the impact of parent-child contact, future orientation, and self-esteem on students’ learning behavior: A mediation analysis. Acta Psychol (Amst). 2025 Feb;252:104683.
23. Musa MH, Salam S, Fesol SFA, Shabarudin MS, Rusdi JF, Norasikin MA, et al. Integrating and retrieving learning analytics data from heterogeneous platforms using ontology alignment: Graph-based approach. MethodsX. 2025 Jun;14:103092.
24. Sailer M, Ninaus M, Huber SE, Bauer E, Greiff S. The End is the Beginning is the End: The closed-loop learning analytics framework. Comput Human Behav. 2024 Sep;158:108305.
25. Baker RS. Artificial intelligence in education: Bringing it all together. In 2021.
26. Igbokwe IC. Application of Artificial Intelligence (AI) in Educational Management. International Journal of Scientific and Research Publications. 2023 Mar 24;13(3).
27. Xu X, Dugdale DM, Wei X, Mi W. Leveraging Artificial Intelligence to Predict Young Learner Online Learning Engagement. American Journal of Distance Education. 2023 Jul 3;37(3):185–98.
28. Karan B, Angadi GR. Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review. Bull Sci Technol Soc. 2023;43(3–4):67–85. https://doi.org/10.1177/02704676231224705
29. Kamalov F, Santandreu Calonge D, Gurrib I. New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability. 2023 Aug 16;15(16):12451.
30. Karan B, Angadi GR. Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review. Bull Sci Technol Soc. 2023;43(3–4):67–85. https://doi.org/10.1177/02704676231224705
31. Yue M, Jong MSY, Dai Y. Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review. Sustainability. 2022;2022:15620.
32. Sukardi, Candra O, Mirshad E, Zaus MA, Islami S. Leveraging Smart Home Training Kits as an Innovative Educational Tool to Foster Higher-Order Thinking Skills. Data and Metadata. 2025 Jan 1;4:476.
33. Zaus MA, Eliza F, Andriani C, Candra O, Jalil SA, Zaus AA, et al. Leveraging Algorithm Instruction with AI Chatbots: A Detailed Exploration of Their Impact on Students’ Computational Thinking. International Journal of Information and Education Technology. 2025;15(5):891–901.
34. Misiejuk K, Kaliisa R, Scianna J. Augmenting assessment with AI coding of online student discourse: A question of reliability. Computers and Education: Artificial Intelligence. 2024 Jun;6:100216.
35. Wu Z, Liu M, Ma G. A machine learning-based two-stage integrated framework for cost reasonableness prediction of green building projects. Journal of Building Engineering. 2025 Apr;100:111733.
36. Meng X, Lang Y, Li X, Li Y, Bu Z, Wu Y, et al. Progress in the application of AI in the standardization of traditional Chinese medicine: A review based on machine learning and deep learning. Pharmacological Research - Modern Chinese Medicine. 2025 Jun;100639.
37. Georgara A, Santolini M, Kokshagina O, Jacinta Haux CJ, Jacobs D, Biwott G, et al. Optimising team dynamics: The role of AI in enhancing challenge-based learning participation experience and outcomes. Computers and Education: Artificial Intelligence. 2025 Jun;8:100388.
38. Montessori M, Ambiyar A, Adri J, Wulansari RE, Agni Zaus M, Islam S. Project-Based Learning in Political Sociology Course: Enhancing Student’s Critical Thinking Skills and Learning Outcome. Salud, Ciencia y Tecnología. 2024 Dec 31;4.
39. Favero TG. Using artificial intelligence platforms to support student learning in physiology. Adv Physiol Educ. 2024 Jun 1;48(2):193–9.
40. Chang YS, Tsai MC. Effects of design thinking on artificial intelligence learning and creativity. Educ Stud. 2024 Sep 2;50(5):763–80.
41. Hazari S. Justification and Roadmap for Artificial Intelligence (AI) Literacy Courses in Higher Education. Journal of Educational Research and Practice. 2024 Apr 29;14(1).
42. Islami S, Ambiyar A, Sukardi S, Zaus AA, Zaus AA, Zaus MA. Innovative Tech-Savvy Education: Designing a Smart Assessment System. Journal of Applied Engineering and Technological Science (JAETS). 2024 Dec 15;6(1):537–49.
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Copyright (c) 2025 Mahesi Agni Zaus , Cici Andriani , Ilmiyati Rahmy Jasril, Anggun Agni Zaus, Syaiful Islami, Yee See Khee (Author)
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