The Impact of AI on Educational Assessment: A Framework for Constructive Alignment

TL;DR

This paper proposes a theoretical framework based on Constructive Alignment and Bloom's taxonomy to adapt educational assessments in the era of AI, emphasizing the need for faculty training and structured guidelines to ensure valid evaluation of student learning.

Contribution

It introduces a novel framework integrating AI considerations into assessment design, emphasizing alignment and staff training to address biases and adapt to AI's influence.

Findings

Lecturers' AI acceptance varies with familiarity and usage.

Structured guidelines can promote assessment alignment.

Training improves staff's adaptation to AI in education.

Abstract

The influence of Artificial Intelligence (AI), and specifically Large Language Models (LLM), on education is continuously increasing. These models are frequently used by students, giving rise to the question whether current forms of assessment are still a valid way to evaluate student performance and comprehension. The theoretical framework developed in this paper is grounded in Constructive Alignment (CA) theory and Bloom's taxonomy for defining learning objectives. We argue that AI influences learning objectives of different Bloom levels in a different way, and assessment has to be adopted accordingly. Furthermore, in line with Bloom's vision, formative and summative assessment should be aligned on whether the use of AI is permitted or not. Although lecturers tend to agree that education and assessment need to be adapted to the presence of AI, a strong bias exists on the extent to which lecturers want to allow for AI in assessment. This bias is caused by a lecturer's familiarity with AI and specifically whether they use it themselves. To avoid this bias, we propose structured guidelines on a university or faculty level, to foster alignment among the staff. Besides that, we argue that teaching staff should be trained on the capabilities and limitations of AI tools. In this way, they are better able to adapt their assessment methods.