A Methodological Framework for AI-Integrated Alternative Assessments in Engineering Education

Recent trends indicate a significant rise in artificial intelligence (AI) usage in engineering education, highlighting the need for alignment with the Fourth Industrial Revolution (IR4.0) and 21st-century skills. Traditional assessment methods often fail to promote higher-order thinking skills needed to solve complex real-world problems. This paper presents the 2A2CAI framework, a methodological approach for alternative assessment in culminating courses (2C) within Malaysian engineering programs. The framework addresses specific gaps, such as the lack of personalization and real-time feedback in current assessments, by leveraging AI to enhance precision and efficiency. The development process includes five stages: a comprehensive literature review, qualitative focus group investigations, quantitative pilot studies, the creation of customized assessment tools, and expert validation. By incorporating AI, the framework ensures robust and reliable assessments that evaluate both cognitive and noncognitive skills, transforming educational evaluations to better meet individual student needs and align with desired learning outcomes.