Program Educational Objectives & Program Outcomes

Program Educational Objectives (PEOs)

1. PEO 1: Core Competence and Lifelong Learning

   Graduates will possess strong foundational knowledge in engineering sciences and emerging technologies such as AI and ML, enabling them to pursue higher studies, research, or adapt to new technological advancements throughout their careers.

2. PEO 2: Professionalism and Ethical Practice

   Graduates will demonstrate ethical behavior, effective communication, teamwork, and leadership skills while applying technological solutions that benefit society and the environment.

3. PEO 3: Innovation and Problem Solving

   Graduates will develop innovative solutions to complex engineering problems through critical thinking, data-driven approaches, and interdisciplinary collaboration.

4. PEO 4: Industry and Global Readiness

   Graduates will be equipped with practical skills, industry experience, and global perspectives to meet the demands of modern technology-driven industries, particularly in AI, ML, and Data Science.

Program Outcomes (POs)

(Aligned with NBA’s 12 standard outcomes, adapted for AI/ML relevance)

1. Engineering Knowledge

   Apply knowledge of mathematics, science, engineering fundamentals, and emerging technologies to solve complex engineering problems.

2. Problem Analysis

   Identify, formulate, research, and analyze engineering problems using AI/ML techniques and data interpretation tools.

3. Design/Development of Solutions

   Design system components and solutions using intelligent systems that meet societal and industrial needs.

4. Conduct Investigations of Complex Problems

   Use research-based knowledge, including design of experiments, analysis, and interpretation of data using AI-driven tools.

5. Modern Tool Usage

   Create, select, and apply appropriate AI/ML frameworks, computational tools, and IT platforms for engineering tasks.

6. The Engineer and Society

   Apply reasoning informed by contextual knowledge to assess societal, legal, and cultural issues related to AI and technology deployment.

7. Environment and Sustainability

   Understand the impact of AI systems in societal and environmental contexts, demonstrating knowledge of sustainable development.

8. Ethics

   Apply ethical principles and commit to professional ethics in data handling, AI system design, and algorithmic transparency.

9. Individual and Team Work

   Function effectively as an individual, and as a member or leader in diverse teams and multidisciplinary settings.

10. Communication

    Communicate effectively with engineering and non-engineering audiences regarding technical information, including AI models and results.

11. Project Management and Finance

    Demonstrate knowledge of engineering and management principles in team projects, startups, or industrial AI/ML ventures.

12. Lifelong Learning

    Recognize the need for, and engage in, independent and lifelong learning to stay current with evolving AI and digital technologies.