We discuss our findings by using them to highlight ways in which engineering educators are already thinking effectively, to suggest how the adoption of innovation and professional problem‐solving can

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• A Systematic Literature Review on the Role of Engineering Education in an Evolving Educational Landscape (2026) · doi

  As the demands placed upon engineers become increasingly complex and global, engineering education must evolve toward models that are adaptive, interdisciplinary, and ethically grounded. This review highlights three future directions that are shaping the trajectory of the discipline, namely: (1) interdisciplinary and systems-based learning, (2) AI-driven personalised education, and (3) global and cultural adaptability. 4.1 Interdisciplinary and Systems-Based Learning Engineering challenges, ranging from climate adaptation to digital transformation, require solutions that integrate knowledge across multiple domains. Universities are thus shifting from siloed disciplinary approaches toward interdisciplinary project-based learning (iPBL) models that merge engineering, social sciences, and design thinking. The MIT NEET (New Engineering Education Transformation) program exemplifies this trend by structuring curricula around thematic “threads” (as energy, mobility, or living systems) that combine mechanical, electrical, and biological principles [39]. Students exposed to iPBL environments demonstrate stronger innovation capacity, systems thinking, and problem contextualisation [40]. However, institutional barriers, such as rigid accreditation requirements and compartmentalised faculty expertise are continue to impede widespread adoption. To overcome these, universities should develop flexible curricular architectures that allow cross-departmental collaboration while maintaining core engineering rigour. 27 Zuraida Ahmad, et al. / A Systematic Literature Review on the Role of Engineering Education in… 4.2 AI-Driven and Data-Informed Personalised Learning Artificial intelligence is redefining how learning is delivered, assessed, and optimised. AI-driven adaptive platforms can analyse learners’ performance in real time, adjust content sequencing, and provide personalised feedback loops. Merino [25] reported significant gains in student engagement and achievement within AI-supported engineering MOOCs. Beyond adaptation, AI also enables learning analytics that inform curriculum design, identifying systemic weaknesses and predicting student performance trends. Yet, this transformation must be guided by ethical safeguards, ensuring algorithmic transparency, data protection, and avoidance of bias [41]. Future research should therefore focus on the development of AI ethics frameworks specific to educational contexts and explore how human–AI collaboration can enhance, rather than replace, the mentoring role of educators. 4.3 Globalisation and Cultural Adaptability Modern engineering practice is inherently global. Engineers must collaborate across cultures, navigate diverse regulatory contexts, and design solutions for varied socio-environmental settings. Consequently, globa

  Keywords: engineering learning education interdisciplinary systems global must based driven personalised transformation design engineers toward models
• From conceptual understanding to interdisciplinary innovation: the mediating mechanism of IESR among high school students (2026) · doi

  integrating assumption different conflicts or methodological differences between disciplines, and forming a unified framework; and innovative application and solution generation, applying integrated knowledge to novel solutions, and evaluating their contexts, designing practical feasibility and innovativeness. resolution, reconciling integration and conflict (Repko and Szostak, the operational perspectives, disciplinary 2020): 2023) innovative (Nersessian and Patton, Recent research shows that interdisciplinary problem-solving is significantly positively correlated with creative thinking, as authentic innovation often occurs at disciplinary intersections (Kelley and Knowles, 2016) conceptual integration ability in scientific innovation teams is a key predictor of breakthrough discoveries interdisciplinary project-based learning in STEM fields significantly enhances thinking and problem-solving abilities students’ (Kwon and Lee, 2025). However, many students a “knowledge island” phenomenon in interdisciplinary tasks- despite mastering disciplinary knowledge, they cannot effectively integrate it (Spelt et al., 2009) even in contexts explicitly requiring interdisciplinary thinking, students still tend to adopt single- disciplinary perspectives (Darbellay, 2015). These findings suggest that IIPI does not automatically emerge from disciplinary knowledge accumulation but requires specific cognitive and metacognitive mechanisms to support cross-boundary knowledge transfer and creative recombination (Darbellay, 2022). exhibit 2.4 Theoretical integration and conceptual model construction

  Keywords: knowledge disciplinary interdisciplinary integration thinking students innovative contexts perspectives problem solving signi cantly creative innovation
• Investigating the Teaching Concerns of Engineering Educators (2007) · doi

  We discuss our findings by using them to highlight ways in which engineering educators are already thinking effectively, to suggest how the adoption of innovation and professional problem‐solving can serve as promising frameworks for thinking about teaching activity, and to suggest that additional research on engineering teaching take advantage of distributed cognition models to truly understand how our students are taught.

  Keywords: engineering thinking suggest teaching discuss using them highlight ways educators already effectively adoption innovation professional