Mitigating Cognitive Biases in Education: The Role of AI-Generated Quizzes in Enhancing Student Learning Outcomes
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.
Authors
- Vinita Agrawal (ORCID: https://orcid.org/0000-0003-1806-7142)
- Arpana Agrawal (ORCID: https://orcid.org/0000-0002-0313-793X)
Institutions
- Woodward (United States) (US)
Publication Details
- Journal
- Journal of Student Research
- Published
- 2025-02-28
- DOI
- https://doi.org/10.47611/jsrhs.v14i1.8712
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00