Rethinking Engineering Curriculum and Pedagogy in the AI Era (ICC Blog # 121)

Creative by spm

This is based on my personal journey as a mechanical engineering graduate from the early 1990s (1992–96) in India. And what I see as the curriculum in the present-day engineering colleges in India for a Mechanical Engineering UG level course. Though I am writing this with a lens of Mechanical engineering, this can be easily extrapolated to other streams of engineering as well. This essay is an attempt to reimagine engineering education in light of rapid advancements in artificial intelligence and digital technologies.

Reflecting on a pre-digital, pre-AI era, it traces the evolution of technology from rudimentary Fortran programming on DOS systems to the omnipresence of AI tools today. My lived experience reveals a striking imbalance: despite rigorous training in core mechanical subjects, I’ve relied far more—nearly 85–90%—on skills such as computer proficiency, systems thinking, and soft skills, with core technical knowledge contributing only 10–15% to my professional toolkit. This insight underscores the urgent need for a curriculum that prioritises interdisciplinary foundations alongside domain-specific expertise.

Background: A Personal Evolution in Technology and Education

My engineering education unfolded in a pre-digital India, where computers were rare and often viewed as luxury items rather than essential tools. In my first year (1992), we were introduced to Fortran 77 on DOS-based systems, typing code into clunky terminals with monochrome displays. By mid-program, basic electronics exposed us to 8085 microprocessors and simple assembly language programming. In our final year, we encountered CAD software running on Windows-equipped 486/586 processors—a significant leap into graphical computing. Storage was limited to 1.44 MB floppy disks, later replaced by CDs. Printers were noisy dot-matrix machines fed with perforated paper, and internet access arrived via screeching BSNL modems—sounds that now evoke a nostalgic sci-fi ambience.

To today’s students, this era may seem prehistoric, and rightly so. Over the past three decades, computing power, data transfer speeds, storage capacity, and cloud infrastructure have advanced exponentially. Computers and mobile devices have become ubiquitous, and with a global population nearing 8 billion, affordable devices and declining communication costs have democratized internet access. We now inhabit digital metaverses where virtual avatars interact seamlessly, and AI tools assist in everything from education to healthcare.

Yet, my career trajectory reveals a deeper truth: while trained as a mechanical engineer, the core subjects—thermodynamics, fluid mechanics, machine design—have played a surprisingly minor role in my professional life. Instead, I’ve leaned heavily on computer skills (programming, systems integration), analytical thinking, and soft skills such as communication, collaboration, and adaptability. This personal insight challenges the prevailing notion that every aspiring engineer must pursue computer science. In fact, as AI commoditises basic computing tasks, standalone CS courses may soon become redundant in foundational engineering programs. Large Language Models (LLMs), now rivalling PhD-level expertise, are accelerating this shift by enabling personalised, efficient, and scalable learning.

Challenges in Current Engineering Education

Traditional engineering curricula often emphasise siloed technical depth, neglecting the interdisciplinary skills that dominate real-world success. Graduates may excel in core domains but struggle with data management, human psychology, or economic feasibility—critical competencies in an AI-augmented world. Lecture-centric teaching methods limit exposure to industry practices, while AI’s automation of routine tasks demands a pivot toward critical thinking, ethical oversight, and creative problem-solving.

Without meaningful adaptation, academic institutions risk producing graduates ill-equipped for the future, echoing historical disruptions where resistance to change led to obsolescence. The disconnect between academic training and industry needs is widening, and unless addressed, it could undermine the relevance of engineering education altogether.

Proposed Core Curriculum Additions

To build resilient, future-ready engineers, we must expand the curriculum beyond traditional boundaries. Drawing on timeless engineering pillars—data handling, analysis, imagination, and creation—programs across all streams (mechanical, civil, electrical, etc.) should integrate the following foundational subjects:

• Understanding and Managing Large Amounts of Data: Engineers must be adept at handling data from sensors, simulations, and global networks. This skill is essential for leveraging cloud systems and making informed decisions.

• Critical Thinking and Design Thinking: These frameworks foster innovation and user-centric problem-solving, enabling engineers to create solutions that are both functional and meaningful.

• Basic Psychology: Understanding human behaviour is crucial for designing intuitive products, interfaces, and systems. Ultimately, we are dealing with humans for our products and services.

• Basic Economics: Engineers must grasp market dynamics to ensure their solutions are economically viable and scalable. The global trade is at USD 33 trillion in 2024 and will continue to grow as communication, logistics and travel become easier every year.

• Foreign Languages (e.g., Latin, French, German, Japanese, Chinese, Arabic): Multilingual proficiency enhances global collaboration and cultural sensitivity. The world is an opportunity for our youngsters. This exposure will give them the outlook.

• Non-Cognitive Skills (Social/Human Skills): Research shows that 85% of career success hinges on these skills—communication, empathy, teamwork—aligning with my own experience of relying predominantly on soft and systems abilities.

• Selling Skills: Engineers must be able to pitch ideas, secure funding, and commercialise innovations effectively. To sell is human.

This curriculum reduces emphasis on basic computer science, instead focusing on AI integration, oversight, and ethical application. It reflects the reality that non-core skills have driven my career and will likely shape the careers of future engineers.

Innovative Teaching and Learning Methodology

To harness the full potential of AI in education, we must rethink pedagogy itself. I propose a blended model that mirrors my own career’s evolution:

• 40% Self-Learning with LLMs: AI tutors offer personalised pacing, instant feedback, and access to vast knowledge repositories. This empowers students to learn efficiently and independently.

• 30% Facilitated Discussions: Industry practitioners, rebranded as facilitators, lead case-based discussions that bring real-world relevance to academic concepts. These sessions foster critical thinking and contextual understanding.

• 30% Experiential Projects or Part-Time Jobs: Hands-on experience through internships, freelance work, or campus-based projects allows students to apply their learning, earn income, and build portfolios.

This structure cultivates adaptability, innovation, and human-centred impact. It also strengthens industry-academia ties, ensuring that education remains aligned with evolving professional demands.

Implementation Considerations

Transitioning to this model requires thoughtful planning and collaboration:

• Pilot Programs: Begin with select institutions and departments, integrating AI tools and industry partnerships to test and refine the approach.

• Faculty Development: Recruit compensated industry experts as facilitators and train academic staff in discussion-based teaching and AI integration.

• Infrastructure: Ensure equitable access to LLMs, devices, and high-speed internet, bridging the digital divide.

• Assessment: Replace rote exams with project-based evaluations, portfolios, and AI-assisted grading to measure real-world competencies.

• Ethics and Equity: Embed principles of fairness, transparency, and inclusivity in AI usage, addressing potential biases and access gaps.

Expected Benefits

Graduates will emerge as versatile professionals, equipped to collaborate with AI, navigate complex systems, and drive meaningful change. Institutions will gain relevance, attract industry partnerships, and position themselves as innovation leaders. Society, in turn, benefits from ethical, impactful engineering solutions that address pressing global challenges.

This model also democratizes education, allowing students from diverse backgrounds to learn at their own pace, access global knowledge, and contribute meaningfully to their communities. It fosters lifelong learning, resilience, and a mindset of continuous improvement—qualities essential in an era of rapid technological change.

Conclusion

Drawing from my 1990s education and subsequent career, where soft and systems skills eclipsed core technical training, this essay urges a fundamental rethink of engineering curriculum and pedagogy. Professors, vice-chancellors, and educators: let us pilot this model, collaborate with industry, and prepare engineers not just for jobs, but for leadership in an AI-driven world. The future demands adaptability, empathy, and interdisciplinary fluency. By embracing this transformation today, we ensure that tomorrow’s engineers are not just technically competent but holistically empowered to thrive.

I am available for a discussion and co-developing the curriculum for your institution along with your academic deans and HODs.