By Lilian H. Hill
Knowledge assessment determines how well students have learned and evaluates the effectiveness of teaching content and strategies for future improvement (Hill, 2020). Research has shown that incorporating knowledge assessments and effective feedback during instruction can boost both student motivation and overall learning effectiveness (Minn, 2022). AI innovations in education promise faster, scalable, and personalized guidance for learning. While AI-based automation can reduce the labor-intensive aspects of conducting learning assessments, its true value lies in enabling a deeper understanding of students and freeing up time to respond creatively to teachable moments. A key priority with AI is ensuring that humans remain actively involved and in control, with attention given to all those participating in the process—students, educators, and others who support learners (U.S. Department of Education, 2023). This blog post explores the opportunities and limitations of using AI for feedback and assessment, along with best practices for effective integration.
Opportunities
AI-driven personalized inputs are revolutionizing education by creating dynamic, tailored learning experiences that foster student engagement, improve learning outcomes, and equip individuals with the skills needed to thrive in a rapidly evolving world. AI recognizes patterns within data and automates decisions to create an adaptive learning environment, a technology-enhanced educational system that uses data and algorithms to personalize instruction in real time, based on each learner’s performance, needs, and preferences. Effective adaptive learning environments depend on three key adaptations: (a) delivering precise, timely, and meaningful feedback during problem-solving, and (b) organizing learning content to match each student’s unique skill level and proficiency, and (c) enhancing formative assessment feedback loops.
1. Timely and Scalable Feedback
AI feedback leverages advancements in natural language processing to provide automated, personalized evaluations that can be scaled according to predefined criteria. AI systems can deliver instant feedback at scale, which is valuable in large classes or for repetitive tasks. According to a 2025 review of educational measurement technology, AI-powered scoring and personalized feedback enhance consistency and speed in assessment delivery. Drawing on extensive linguistic databases, these systems generate responses that mimic human engagement with student work. This technology has sparked considerable discussion in academic contexts, with the potential to transform teaching and learning practices (Zapata et al., 2025).
2. Personalized Input and Adaptive Growth
Adaptive learning systems are essential for delivering personalized experiences in online instruction, particularly for those courses with large enrollment, such as MOOCS or Intelligent Tutoring Systems. For example, in a randomized controlled trial involving 259 undergraduates, researchers found that students receiving AI-generated feedback showed significant improvements across various writing dimensions compared to traditional instruction, with particularly strong effects on organization and content development (Zhang, 2025). The intervention also revealed that students valued usefulness over surface ease of use.
3. Enhanced Formative Assessment Loops
Technological interventions can create more personalized, timely feedback loops that facilitate deeper engagement with learning. Formative assessment has long been a central application of educational technology, as feedback loops are essential for enhancing teaching and learning. AI may enable richer feedback loops by supporting formative assessment—when paired intentionally with human oversight—helping teachers adapt instruction based on student progress.
Limitations and Key Concerns
Creating machine learning models that deliver meaningful, personalized, and authentic feedback demands substantial involvement from human domain experts. Choices about whose expertise is included, how it is gathered, and when it is significantly applied influence the relevance and quality of the feedback produced. These models also require ongoing maintenance and refinement to align with changing contexts, evolving theories, and diverse student needs. Without continuous updates, feedback can quickly become outdated or misaligned with current learner requirements. Key limitations include (a) concerns about AI system accuracy, (b) loss of contextual understanding and embedded bias, (c) overreliance that diminishes human interaction, and (d) important ethical and pedagogical challenges.
1. Accuracy
Researchers have recorded numerous cases of AI systems making harmful decisions due to coding errors or biased training data. Such failures have rendered inaccurate teaching evaluations, caused job and license losses, and discriminated based on names, addresses, gender, and skin color. AI systems can sometimes exploit shortcuts without capturing the deeper intent of their designers or the domain’s full complexity. For instance, a 2017 image recognition system “cheated” by identifying a copyright tag linked to horse images instead of learning to recognize images of horses (Sample, 2017).
2. Context Loss and Bias
Lindsay et al. (2025) note that the convenience of automation carries the risk of neglecting the distinct needs of minority or atypical learners because they are more difficult to standardize and address. For example, automated essay scoring (AES) systems often rely on surface features like essay length or keywords, making them insensitive to nuance, creativity, and accurate content understanding. In experiments with several chatbots, Taylor (2024) found that AI-generated feedback tends to be generic and provide variations of the same feedback for multiple students (Taylor, 2024). Algorithmic bias is also a concern. Models trained on unbalanced data can amplify cultural or linguistic disparities, potentially disadvantaging Black, Indigenous, and People of Color (BIPOC) or non‑native English speakers unless bias mitigation strategies are in place.
3. Over-reliance and Reduced Human Interaction
Evidence suggests that when students depend too heavily on AI-generated feedback, their opportunities for critical reflection and dialogue diminish, both key foundations for higher-order thinking and deep learning. A recent comparative study found that students tend to mistrust AI feedback when it is not combined with human guidance, while academic staff were more open, especially if AI suggestions augmented rather than replaced instructor feedback (Henderson et al., 2025). Moreover, educators’ reflections indicate that adopting AI for meaningful feedback may serve to increase instructor workload and complexity compared to traditional teaching methods, especially when contextual interpretation is needed (Taylor, 2024).
4. Ethical and Pedagogical Considerations
Generative AI tools raise essential ethical dimensions—notably involving participation, impact, fairness, and evolution over time. Unless systems are carefully designed to be inclusive, AI-generated feedback may marginalize minority learners with unique needs (Lindsay et al., 2025). The National Council on Educational Measurement’s AIME group has similarly stressed validity, equity, and transparency as pillars for responsible AI in educational measurement (Bulut et al., 2024). With thoughtful implementation, ethical frameworks, educator training, and human oversight, AI can enhance education without sacrificing critical thinking or integrity.
Best Practices for Implementation
- Keep humans in the loop. Use AI as a supplement, not a replacement, for instructor-led feedback and assessment.
- Pilot first. Collect user feedback on pilot deployments before full-scale adoption to ensure transparency, acceptance, and reliability.
- Disclose AI use. State clearly when AI tools produce summaries or initial feedback, including platform and prompt details when appropriate.
- Educate users. Teach students to interpret AI output critically and support educators in leveraging feedback meaningfully.
- Audit for bias and fairness. Apply algorithmic audits and explainable AI techniques to evaluate model performance across diverse groups.
References
Bulut, O., Beiting-Parrish, M., Casablanca, J. M., Slater, S. C., Jiao, H., Song, D. … Morilova, P. (2024). The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges. Journal of Educational Measurement and Evaluation, 5(3). https://doi.org/10.59863/miql7785
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing Generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 1–16. https://doi.org/10.1080/02602938.2025.2502582
Hill, L. H. (Ed.). (2020). Assessment, evaluation, and accountability in adult education. Stylus Publishing.
Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050. https://doi.org/10.1016/j.caeai.2022.100050
Sample, I. (5 November 2017). Computer says no: Why making Ais fair, accountable, and transparent is critical. The Guardian. https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial
Taylor, P. (2024, September 6). The imperfect tutor: Grading, feedback and AI. Inside Higher Education. https://www.insidehighered.com/opinion/career-advice/teaching/2024/09/06/challenges-using-ai-give-feedback-and-grade-students?utm_source=chatgpt.com
U.S. Department of Education, Office of Educational Technology (2023). Artificial intelligence and future of teaching and learning: Insights and recommendations. Washington, DC.
Zapata, G. C., Cope, B., Kalantzis, M., Tzirides, A. O. (Olnancy), Saini, A. K., Searsmith, D., … Abrantes da Silva, R. (2025). AI and peer reviews in higher education: Students’ multimodal views on benefits, differences and limitations. Technology, Pedagogy and Education, 1–19. https://doi.org/10.1080/1475939X.2025.2480807
Zhang, K. (2025). Enhancing Critical Writing Through AI Feedback: A Randomized Control Study. Behavioral Sciences, 15(5):600. https://doi.org/10.3390/bs15050600
