Engineering Education in the Age of AI: Protecting Learning Outcomes while Teaching Responsible Use

Malaysian engineering education is now under two pressures at the same time. Accreditation instruments, chiefly the Engineering Accreditation Council's 2024 Programme Accreditation Standard (EAC 2024) and the Malaysian Qualifications Framework (MQF 2024) of the Malaysian Qualifications Agency, expect graduates to show more than technical competence: ethical judgement, teamwork, communication and sustainability awareness now count as outcomes in their own right. Over roughly the same period, generative artificial intelligence has made it possible for a student to hand in competent-looking coursework without doing the reading and reasoning the assignment was meant to require. The risk is concrete. A student can leave a course knowing less than the curriculum intended while the submitted work looks accomplished. We ask what these two shifts mean when read together, and what they imply for teaching and assessment. Drawing on 36 peer-reviewed, openly accessible sources published between 2019 and 2026, together with the EAC 2024 and MQF 2024 standards, we examine five curriculum strategies that recur in the literature: project-based learning (PBL), service-learning through Malaysia's SULAM framework, value-based education, integration of the UN Sustainable Development Goals (SDGs), and communication-skills instruction. We read them as related responses to one problem. Each depends on a process the student has to go through over time, which is harder to bypass than a single artefact handed in at the end, and each builds outcomes that current accreditation treats as central. Most existing studies treat these themes separately, both from each other and from the assessment concerns raised by AI. We propose an integrative reading in which SULAM-linked, SDG-framed PBL projects, assessed with explicit communication criteria and a documented reasoning process, give programmes a workable route to these outcomes that does not rest on trust alone. The paper ends with practical implications for curriculum and assessment design in Malaysian engineering faculties and points out where local evidence on AI-integrated assessment is still thin.

engineering education, EAC 2024, project-based learning, service-learning, artificial intelligence

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