How many of you follow competency and performance indicators-based teaching-learning and assessment in your institute? I know about health sciences. Their entire Outcome Based Education (OBE) is competency-based medical education (CBME) model, and I found it very useful. After all, Engineering and technology is no less skilling.

Let’s see how competencies can be the new currencies of OBE and why these should not be restricted only to the health sciences.

Someone might ask, after all, when we have Bloom-aligned Course Outcomes (COs), then why competencies ? Why are you making our lives difficult ?

Well, COs as defined are specific statements. They certainly require one more step to capture what’s desired to be accomplished. Well, a possible counter to this could be, why get focused and deep, why not wide? I think skilling is always deep and compelling focused. In the current times, the youth in India, needs to quickly skill and innovate before India goes old.

Competencies help break down COs into doable skills and learning. It helps a teacher to build her/his own specific knowledge/skill competency and then deliver focused instructions. It further helps in planning students’ assessment through Performance Indicators (PIs). B’cause competencies are assessed in the graded levels/degrees, and not a generic measure of marks alone, it helps monitor the level of skill better and results in attain its linked CO, far more effectively.

Bloom’s taxonomy is primarily cognitive. Soft skills, behaviour, attitude are perhaps better handled by competencies by designing suitable performance indicators and rubrics of assessment.

Are you then ready to adapt to a new course plan based on competencies and PIs.? If ready, I have made one such a course plan, based on my limited knowledge on the AI. You would be free to improve it. It is only to give you an idea on how to develop a new structure of a course by your faculty in your institute. If you feel, COs statements are not enough to help you develop and deliver your course powerfully, try incorporating competencies and PIs, while making your sessional/topic plan and then deliver your courses. Receive feedback and celebrate the success of a new better pedagogical enabler in your hands !

Below is Competency-based AI course plan.

Name of the Course: AI : Fundamentals, Concepts & Application

Course Objectives and Learning Outcomes

This course is designed to help students:

• Understand the evolution, paradigms, and societal impact of Artificial Intelligence.
• Apply problem-solving strategies, search algorithms, and logic-based reasoning.
• Implement core machine learning algorithms with real-world datasets.
• Design and evaluate neural networks for vision and text applications.
• Examine ethical issues and emerging trends shaping the future of AI.

Course Units with Outcomes, Competencies and Performance Indicators

Unit 1: Foundations of AI

This unit introduces the foundational concepts of Artificial Intelligence, including its history, key milestones, and major subfields such as Machine Learning and Deep Learning. Students will also learn about intelligent agents and real-world applications of AI.

Course Outcome: CO1: Understand and explain foundational concepts and evolution of AI.

Unit 2: Search & Reasoning

Covers basic AI problem-solving techniques including uninformed and informed search strategies, along with the fundamentals of propositional and first-order logic for reasoning.

Course Outcome: CO2: Apply problem-solving strategies and logic-based reasoning in AI.

Unit 3: Machine Learning

Introduces machine learning workflows including supervised (classification and regression) and unsupervised (clustering) approaches with model evaluation metrics and implementation using Python.

Course Outcome: CO3: Analyze and implement basic ML models using real data.

Unit 4: Neural Networks and Deep Learning

Focuses on neural network architectures, training deep learning models with backpropagation, convolutional layers, and applications in vision and text processing.

Course Outcome: CO4: Design and evaluate neural networks for AI applications.

Unit 5: Ethics, Applications, and Future Trends

Examines ethical, societal, and regulatory challenges of AI including bias, transparency, and accountability, and explores emerging trends such as explainable AI and AGI.

Course Outcome: CO5: Evaluate ethical issues and AI’s future implications.

Let me know your views on the pedagogy.

Prof JR Sharma-Mentor to HEIs