Engineering education in developing countries faces critical challenges that hinder progress toward achieving the United Nations Sustainable Development Goals (SDGs). In the Democratic Republic of Congo (DRC), students entering engineering programs often exhibit significant apprehension toward foundational sciences, creating barriers to developing the technical competencies required for sustainable development. This paper introduces the AI-Integrated Practical Learning in Engineering (AIPLE) Framework, an innovative pedagogical model that synergizes Problem-Based Learning (PBL), hands-on experimentation, and strategic Artificial Intelligence (AI) integration to transform engineering education for sustainability. The AIPLE framework employs a five-stage cyclical process designed to address student apprehension while fostering sustainable engineering mindsets essential for achieving SDGs 4 (Quality Education), 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), and 11 (Sustainable Cities and Communities). This study, grounded in qualitative surveys of engineering instructors at Université Loyola du Congo (ULC), demonstrates how the framework addresses pedagogical limitations while building technical competency and sustainability consciousness. The research reveals that traditional didactic methods inadequately prepare students for complex sustainability challenges, while the AIPLE framework’s integration of AI-assisted learning, practical problem-solving, and sustainability-focused projects offers a scalable solution for engineering education transformation in resource-constrained environments. Our findings indicate strong instructor support for PBL methodologies and cautious optimism regarding AI integration, with emphasis on addressing infrastructure and ethical considerations. The AIPLE framework contributes to sustainable development by preparing engineers who are technically competent and committed to creating environmentally responsible, socially inclusive, and economically viable solutions for developing countries.
Tshikolu et al. (2025) studied this question.