BOARD OF STUDIES – DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING (ARTIFICIAL INTELLIGENCE)

The Board of Studies (BoS) of the Department of Computer Science and Engineering (Artificial Intelligence) is responsible for designing, developing, reviewing, and updating the academic curriculum to meet current technological advancements, industry requirements, and academic standards. It ensures that the curriculum promotes strong foundations in Computer Science and Engineering, Artificial Intelligence, Machine Learning, Deep Learning, and other emerging technologies.

Composition of the Board of Studies

The Board of Studies shall comprise the following members:

1. Head of the Department – Chairperson.
2. Two senior faculty members from the Department of Computer Science and Engineering (Artificial Intelligence).
3. Two faculty members representing relevant specializations and course coordinators.
4. Two subject experts from reputed academic institutions or research organizations.
5. One nominee of the University.
6. One industry expert from a reputed IT organization or Artificial Intelligence industry.
7. One meritorious alumnus of the Department.

Term of the Board of Studies

The term of the Board of Studies shall be three years, subject to the applicable university regulations and approval by the competent authority.

Meetings of the Board of Studies

The Board of Studies shall ordinarily meet at least once during each academic year. Additional meetings may be convened whenever necessary to discuss curriculum revision, introduction of new courses, academic reforms, and other matters related to the development of the department.

Meetings shall be convened by the competent authority in consultation with the Chairperson of the Board of Studies and in accordance with the university’s rules and procedures.

Functions of the Board of Studies

The Board of Studies shall perform the following functions:

1. To prepare, review, and revise the curriculum for undergraduate and postgraduate programmes offered by the Department of Computer Science and Engineering (Artificial Intelligence).
2. To recommend the introduction of new courses and programmes in emerging areas such as Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Natural Language Processing, Computer Vision, Data Science, and Robotics.
3. To ensure that the curriculum provides a strong foundation in programming, data structures, algorithms, database management systems, operating systems, computer networks, and software engineering.
4. To incorporate industry-oriented courses, emerging technologies, professional skills, and practical applications into the curriculum.
5. To design and revise course objectives, Course Outcomes (COs), Programme Outcomes (POs), and Programme Specific Outcomes (PSOs) in accordance with Outcome-Based Education (OBE) principles.
6. To recommend suitable teaching-learning methodologies, laboratory experiments, project work, internships, and other experiential learning activities.
7. To review the assessment and evaluation methods, including internal assessments, semester-end examinations, practical examinations, projects, and presentations.
8. To recommend textbooks, reference books, online learning resources, and other instructional materials for various courses.
9. To suggest suitable question paper setters, examiners, evaluators, and other academic experts in accordance with university regulations.
10. To review the academic performance of students and recommend measures to improve learning outcomes and academic achievement.
11. To recommend the introduction of multidisciplinary and interdisciplinary courses in areas such as Artificial Intelligence, Internet of Things (IoT), Big Data Analytics, Cybersecurity, Cloud Computing, and Intelligent Systems.
12. To promote research, innovation, product development, entrepreneurship, and student participation in technical competitions and research activities.
13. To encourage collaboration with industries, research institutions, and other academic organizations for internships, projects, industrial visits, guest lectures, and collaborative research.
14. To recommend improvements in laboratory facilities, computing infrastructure, AI development platforms, software tools, and other resources required for effective teaching and research.
15. To review and recommend measures for achieving Course Outcomes, Programme Outcomes, and Programme Specific Outcomes.
16. To incorporate ethical and responsible AI practices, data privacy, cybersecurity, fairness, transparency, and accountability into the curriculum.
17. To review the relevance of the curriculum with respect to industry expectations, higher education, competitive examinations, research opportunities, and employability.
18. To recommend skill-development programmes, certification courses, workshops, seminars, and faculty development activities in emerging areas of Computer Science and Artificial Intelligence.
19. To suggest measures for continuous curriculum improvement based on feedback from students, faculty, alumni, employers, and academic experts.
20. To consider and recommend any other academic matters referred to the Board of Studies by the University or the competent authority.