Transformation from
Traditional Assessments to
AI-Adapted Assessments
This page outlines the transition from traditional assessments to AI-adapted assessments, highlighting the integration of cutting-edge generative AI (GenAI) tools that provide real-time feedback and personalised planning. Traditional assessments are increasingly limited by their limited adaptability and lack of contextual flexibility. By leveraging advanced GenAI tools specifically designed for today’s AI-driven world, educators can provide real-time feedback and tailor learning plans to individual needs with greater precision.
- Heavy weighting on the written report
- Validity drift in learning measurement
- Authorship and integrity uncertainty
- Limited visibility of learning processes
- Reduced quality and diagnostic value of feedback
- Grade inflation and overestimation of mastery
- Development of real‑world communication skills
- Higher-order thinking
- Reduction of plagiarism risk
- Validity drift in assessing capstone learning outcomes
- Authorship, integrity, and contribution ambiguity
- Limited visibility of the learning and design process
- Reduced quality and specificity of supervisory feedback
- Grade inflation and overestimation of capstone mastery
- Equity and access disparities in AI use
- Validity drift in measuring learning outcomes
- Authorship, integrity, and accountability gaps
- Reliance on passive or product‑based participation
- Reduced visibility into learning processes
- Grade inflation and surface‑level achievement
- Equity and access concerns in AI use
- Time‑consuming and resource‑intensive
- Validity limitations in measuring learning outcomes
- Limited insight into authorship and response authenticity
- Low diagnostic value and generic feedback
- Inflexibility regarding learner context and emerging knowledge
- Overestimation of understanding
- Difficulty in assessing higher‑order thinking
- Validity drift in assessing intended learning outcomes
- Authorship and integrity challenges
- Limited visibility of learning processes
- Grade inflation and misrepresentation of mastery
- Vulnerability to AI‑assisted cheating in online formats
- Validity drift in measuring intended learning outcomes
- Surface learning and memorisation bias
- Misalignment with real‑world and graduate skills
- Limited feedback and learning value
- Grade inflation and misrepresentation of competence
- Heavy weighting of written reports
- Validity drift in measuring group and individual learning
- Authorship, integrity, and contribution ambiguity
- Difficulty in assessing individual understanding
- Loss of visibility of collaborative processes
- Grade inflation and overestimation of team performance
- Reduced weighting for written reports
- Emphasising reflections, presentations, or oral defences
- Contribution logs or reflections to ensure authentic understanding
- Group documentation of when and how AI is used
- Human-led final analysis with AI assistance
References:
Khatri, B. B., & Karki, P. D. (2023). Artificial intelligence (AI) in higher education: Growing academic integrity and ethical concerns. Nepalese Journal of Development and Rural Studies, 20(1), 1-7.
Kafali, E., Preuveneers, D., Semertzidis, T., & Daras, P. (2024). Defending Against AI Threats with a User-Centric Trustworthiness Assessment Framework. Big Data and Cognitive Computing, 8(11), 142. https://doi.org/10.3390/bdcc8110142
Eaton, S. E., Crossman, K., & Edino, R. (2019). Academic Integrity in Canada: An Annotated Bibliography. Retrieved from https://files.eric.ed.gov/fulltext/ED593995.pdf.