Mitigating Cognitive Biases in Education: The Role of AI-Generated Quizzes in Enhancing Student Learning Outcomes
DOI:
https://doi.org/10.47611/jsrhs.v14i1.8712Keywords:
Ai, Education, Quizzes, Cognitive BiasesAbstract
The study of learning biases school students face is gaining more attention in education psychology. The present research explores how different cognitive biases influence how students perceive, process, and retain information. Various learning biases, such as confirmation bias, Dunning-Kruger effect, Halo effect, sunk cost fallacy, bandwagon effect, self-serving bias, fixed mindset, recency effect, anchoring bias, and others, are responsible for shaping students' educational experiences as they affect student motivation, engagement, and self-efficacy.
AI-generated quizzes are a powerful tool that helps students overcome learning biases because such quizzes provide personalized, adaptive learning experiences that challenge existing misconceptions and promote more profound understanding. AI quiz ensures the learning process is customized to meet students' needs and knowledge gaps. AI quizzes ensure that every student is challenged appropriately by using adaptive algorithms that can adjust the difficulty level of questions per student's performance. These quizzes also provide immediate, comprehensive, and unbiased feedback, which helps students identify their areas of strength and areas where they need to improve.
In conclusion, AI-generated quizzes pose a highly promising strategy in education. They mitigate cognitive biases by encouraging students to have a growth-oriented mindset and improve their learning experience. As educators leverage AI technology, educational excellence is seeing a new era where every student gets an equal opportunity to achieve academic success and unlock their full learning potential.
Downloads
References or Bibliography
Ahmed, S., Khalil, M. I., Chowdhury, B., Haque, R., bin S Senathirajah, A. R., & bin Omar Din, F. M. (2022). Motivators and barriers of artificial intelligent (AI) based teaching. Eurasian Journal of Educational Research, 100(100), 74-89.
Alam, A. (2023). Harnessing the Power of AI to Create Intelligent Tutoring Systems for Enhanced Classroom Experience and Improved Learning Outcomes. In Intelligent Communication Technologies and Virtual Mobile Networks (pp. 571-591). Singapore: Springer Nature Singapore.
Areed, M. F., Amasha, M. A., Abougalala, R. A., Alkhalaf, S., & Khairy, D. (2021). Developing gamification e-quizzes based on an android app: the impact of asynchronous form. Education and Information Technologies, 26, 4857-4878.
Bianco, M. (2021). Overcoming the social barriers of AI adoption (Doctoral dissertation, Master’s Thesis, Eindhoven University of Technology] https://research. tue. nl/en/studentTheses/overcoming-the-social-barriers-of-ai-adoption).
Bognár, L., Fauszt, T., & Váraljai, M. (2021). The impact of online quizzes on student success. International Journal of Emerging Technologies in Learning (iJET), 16(11), 225-244.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational philosophy and theory, 53(12), 1229-1245.
Dee, T. S., & Gershenson, S. (2017). Unconscious bias in the classroom: Evidence and opportunities. Mountain View, CA: Google Inc.
Edeni, C. A., Adeleye, O. O., & Adeniyi, I. S. (2024). The role of AI-enhanced tools in overcoming socioeconomic barriers in education: A conceptual analysis. World Journal of Advanced Research and Reviews, 21(3), 944-951.
Eglington, L. G., & Pavlik Jr, P. I. (2023). How to optimize student learning using student models that adapt rapidly to individual differences. International Journal of Artificial Intelligence in Education, 33(3), 497-518.
Ehrlinger, J., Mitchum, A. L., & Dweck, C. S. (2016). Understanding overconfidence: Theories of intelligence, preferential attention, and distorted self-assessment. Journal of Experimental Social Psychology, 63, 94-100.
Fesakis, G., & Prantsoudi, S. (2021, November). Raising artificial intelligence bias awareness in secondary education: the design of an educational intervention. In ECIAIR 2021 3rd European Conference on the Impact of Artificial Intelligence and Robotics (p. 35). Academic Conferences and publishing limited.
Fitria, T. N. (2021, December). Artificial intelligence (AI) in education: Using AI tools for teaching and learning process. In Prosiding Seminar Nasional & Call for Paper STIE AAS (Vol. 4, No. 1, pp. 134-147).
Garrett, N., Beard, N., & Fiesler, C. (2020, February). More than" If Time Allows" the role of ethics in AI education. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 272-278).
Grimes, A., Medway, D., Foos, A., & Goatman, A. (2017). Impact bias in student evaluations of higher education. Studies in higher education, 42(6), 945-962.
Hahn, M. G., Navarro, S. M. B., Valentín, L. D. L. F., & Burgos, D. (2021). A systematic review of the effects of automatic scoring and automatic feedback in educational settings. IEEE Access, 9, 108190-108198.
He, G., Kuiper, L., & Gadiraju, U. (2023, April). Knowing about knowing: An illusion of human competence can hinder appropriate reliance on AI systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-18).
Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022(1), 5215722.
Hutt, S., & Hieb, G. (2024). Scaling Up Mastery Learning with Generative AI: Exploring How Generative AI Can Assist in the Generation and Evaluation of Mastery Quiz Questions. In Proceedings of the Eleventh ACM Conference on Learning@ Scale (pp. 310-314).
Isman, S. M. (2023). AI-Powered quizzes in online learning: Exploring opportunities and challenges. In International Conference on Teaching and Learning (Vol. 1, pp. 386-396).
Khan, S. (2023). The Ethical Imperative: Addressing Bias and Discrimination in AI-Driven Education. Social Sciences Spectrum, 2(1), 89-96.
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074.
Knyazev, N., & Oosterhuis, H. (2022, August). The bandwagon effect: not just another bias. In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 243-253).
Knyazev, N., & Oosterhuis, H. (2022, August). The bandwagon effect: not just another bias. In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 243-253).
Le Bihan, E. (2024). Navigating artificial intelligence in education: first-and second-order barriers to the implementation of AI in Finnish classrooms.
Lu, O. H., Huang, A. Y., Tsai, D. C., & Yang, S. J. (2021). Expert-authored and machine-generated short-answer questions for assessing students learning performance. Educational Technology & Society, 24(3), 159-173.
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. UCL Knowledge Lab: London, UK.
Mansharamani, V. (2020). Think for yourself: Restoring common sense in an age of experts and artificial intelligence. Harvard Business Press.
Mousavi, R., Raghu, T. S., & Frey, K. (2020). Harnessing artificial intelligence to improve the quality of answers in online question-answering health forums. Journal of Management Information Systems, 37(4), 1073-1098.
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925.
Owoc, M. L., Sawicka, A., & Weichbroth, P. (2019, August). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (pp. 37-58). Cham: Springer International Publishing.
Pendy, B. (2021). Artificial intelligence: The future of education. Jurnal Indonesia Sosial Sains, 2(11).
Rhue, L. (2019). Beauties in the AI of the beholder: how AI anchors subjective and objective predictions. ICIS 2019 Proceedings. 15.
Richie, M., & Josephson, S. A. (2018). Quantifying heuristic bias: anchoring, availability, and representativeness. Teaching and learning in Medicine, 30(1), 67-75.
Sanchez, D. R., Langer, M., & Kaur, R. (2020). Gamification in the classroom: Examining the impact of gamified quizzes on student learning. Computers & Education, 144, 103666.
Santhanam, S., Karduni, A., & Shaikh, S. (2020, April). Studying the effects of cognitive biases in evaluation of conversational agents. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
Schiff, D. (2021). Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI & society, 36(1), 331-348.
Schroeder, K. T., Hubertz, M., Van Campenhout, R., & Johnson, B. G. (2022). Teaching and learning with AI-Generated Courseware: Lessons from the Classroom. Online Learning, 26(3), 73-87.
Seldon, A., Abidoye, O., & Metcalf, T. (2020). The Fourth Education Revolution Reconsidered: Will Artificial Intelligence Enrich or Diminish Humanity? Legend Press Ltd.
Sirois, L. P. (2019). The psychology of sunk cost: A classroom experiment. The Journal of Economic Education, 50(4), 398-409. Ergo: An Open Access Journal of Philosophy, 6:1153-1190.
Srinivasan, V. (2022). AI & learning: A preferred future. Computers and Education: Artificial Intelligence, 3, 100062.
Tahiru, F. (2021). AI in education: A systematic literature review. Journal of Cases on Information Technology (JCIT), 23(1), 1-20.
Tang, K. H. D. (2024). Implications of Artificial Intelligence for Teaching and Learning. Acta Pedagogia Asiana, 3(2), 65-79.
Tsai, D. C., Huang, A. Y., Lu, O. H., & Yang, S. J. (2021, July). Automatic question generation for repeated testing to improve student learning outcome. In 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 339-341). IEEE.
Vincent-Lancrin, S., & Van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges. OECD Education Working Papers, No. 218, OECD Publishing, Paris.
Wang, T., & Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 2, 100031.
Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023a). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), 6716.
Wang, X., Wrede, S., van Rijn, L., & Wöhrle, J. (2023b). AI-Based Quiz System For Personalised Learning. In ICERI2023 Proceedings (pp. 5025-5034). IATED.
Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008.
Zaman, B. U. I. (2024). Transforming education through AI benefits risks and ethical considerations. Preprints, 2024070859.
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021(1), 8812542.
Zubková, N. V. K. (2023). The Potential of Ai Tools In Formal Education. Marketing Identity, 427.
Published
How to Cite
Issue
Section
Copyright (c) 2025 Avyakt Agrawal; Vineet Agrawal
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
