The ALIGNED Framework
Author(s)
Tiwari, Harshita
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Abstract
The adoption of large language models (LLMs) within higher education learning management systems is disrupting conventional views of assessment. While these tools enhance feedback and learning support, most evaluations still focus on metrics like accuracy and grades, making it hard to differentiate between AI-assisted work and genuine student understanding. This paper argues that assessments in LLM-enabled environments should prioritize students' reasoning, reflection, and comprehension, rather than just final outputs. Drawing on AI evaluation theory, sociocultural learning theory, and the Community of Inquiry framework, the study proposes an Ability-Led Assessment approach. This framework includes three levels of evidence: observable results, traces of the learning process, and deeper cognitive and metacognitive skills. It offers a practical toolkit—featuring an assessment matrix and flexible rubrics—to align AI use with educational goals, focusing on intellectual growth and responsible AI integration.
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Date
2026
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Text
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Masters Project
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