Board of Studies – Department of Artificial Intelligence and Machine Learning

The Board of Studies (BoS) is a fundamental component of the academic system of a university. It is responsible for framing syllabi for various courses offered by the Department of Artificial Intelligence and Machine Learning, evaluating and revising syllabi periodically, proposing new courses and programmes of study, determining the details of continuous internal assessment, recommending panels of examiners under the semester system, and ensuring that the curriculum remains aligned with emerging technological advancements, industry requirements, research developments, and academic standards.

Composition of the Board of Studies

The following members shall be nominated by the competent authority of the University, as per the applicable University statutes and regulations.

1. Head of the Department (Chairperson): Head of the Department of Artificial Intelligence and Machine Learning.
2. Two Senior Faculty Members: Two senior faculty members from the Department of Artificial Intelligence and Machine Learning within the University.
3. Two Course Coordinators: Two course coordinator members from the Department of Artificial Intelligence and Machine Learning within the University.
4. Two External Subject Experts: Two subject experts from reputed universities, research institutions, or other recognized academic organizations outside the University, specializing in Artificial Intelligence, Machine Learning, Data Science, or allied areas.
5. One University Nominee: A nominee representing the University.
6. One Industry Representative: One representative from the industry, corporate sector, or an allied area related to Artificial Intelligence, Machine Learning, Data Science, software development, or student placements.
7. One Meritorious Alumnus: One meritorious alumnus of the Department or University with relevant academic, research, entrepreneurial, or industry experience.

Term of Office

The term of the nominated members of the Board of Studies shall be three years (2024–25 to 2026–27), subject to the applicable University rules and regulations.

Meetings

The meeting of the Board of Studies shall ordinarily be convened once in every academic year by the Registrar of the University. The date, time, and venue of the meeting shall be fixed by the Registrar in consultation with the Chairperson of the Board of Studies concerned and with the approval of the Vice-Chancellor.

Additional meetings may be convened whenever necessary to consider curriculum revisions, the introduction of new courses, academic reforms, examination-related matters, or other issues requiring the attention of the Board, in accordance with University regulations.

Functions of the Board of Studies

1. To review and revise the syllabi of the programmes offered by the Department of Artificial Intelligence and Machine Learning, ordinarily once every two or three years, incorporating recent advancements in Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Natural Language Processing, Computer Vision, Data Science, Big Data Analytics, and other relevant areas.
2. To frame guidelines for developing curriculum structures based on learning outcomes and graduate attributes, including blended and online learning modules, add-on courses, teaching and research activities, community engagement, skill development, internships, industry collaborations, interdisciplinary learning, and quality enhancement of the courses and programmes offered by the Department.
3. To prescribe academic performance indicators, assessment methods, and evaluation criteria for courses offered by the Department in accordance with the requirements of relevant professional accreditation agencies and applicable regulatory authorities.
4. To consider and implement the suggestions and guidelines issued by the University Grants Commission (UGC), All India Council for Technical Education (AICTE), and other relevant regulatory authorities from time to time concerning curriculum modernization, academic reforms, emerging technologies, and quality assurance.
5. To review the progress and effectiveness of the existing programmes offered by the Department, identify deficiencies, if any, and recommend appropriate remedial measures to the concerned University authorities to improve the quality, relevance, and academic standards of the programmes.
6. To recommend to the Academic Council, through the Dean of the concerned Faculty, the introduction of new programmes in Artificial Intelligence and Machine Learning and allied areas, including their programme structure, admission criteria, curriculum, credit distribution, scheme of examinations, and other academic requirements.
7. To review question papers from previous examinations, reports of evaluators on student performance, and examination results, and to suggest remedial measures, wherever necessary, to improve the quality of question papers, assessment practices, and student learning outcomes.
8. To recommend annually to the Board of Management a panel of paper setters and examiners, both internal and external, for all theory and practical courses, laboratory examinations, projects, internships, seminars, and viva-voce examinations, as well as adjudicators for M.Phil./Ph.D. theses, wherever applicable under the University regulations.
9. To recommend to the Board of Management a panel of thirty subject experts from reputed universities, research institutions, and recognized organizations, as and when required, for nomination to Selection Committees for the appointment of teachers in Artificial Intelligence and Machine Learning and related disciplines.
10. To consider and offer its opinions and recommendations on matters relating to Artificial Intelligence and Machine Learning and allied disciplines referred to it by the Vice-Chancellor, Board of Management, Academic Council, or the concerned Faculty.
11. To recommend the introduction of emerging and interdisciplinary courses in areas such as Generative AI, Large Language Models, Explainable AI, Responsible AI, Reinforcement Learning, Graph Neural Networks, AI for Healthcare, AI for Cybersecurity, AI for Robotics, and AI for Cloud Computing, based on academic relevance and industry requirements.
12. To promote industry–academia collaboration through internships, industry-sponsored projects, expert lectures, workshops, hackathons, technical training, research collaborations, and placement-oriented skill development programmes.
13. To encourage research and innovation among faculty members and students in Artificial Intelligence, Machine Learning, Deep Learning, Data Science, and related fields, including interdisciplinary research, publications, patents, and technology transfer.
14. To review and recommend improvements in laboratory infrastructure, AI/ML software tools, computing facilities, datasets, cloud platforms, and other resources required for effective teaching, learning, and research.
15. To monitor the attainment of Course Outcomes (COs), Programme Outcomes (POs), and Programme Specific Outcomes (PSOs), wherever applicable, and recommend measures for continuous improvement in teaching, learning, assessment, and curriculum delivery.
16. To ensure that the curriculum addresses ethical AI, data privacy, information security, responsible use of AI, intellectual property rights, professional ethics, and applicable legal and regulatory requirements.
17. To recommend measures for enhancing students’ employability, entrepreneurship, higher education opportunities, professional certifications, and readiness for emerging roles in Artificial Intelligence and Machine Learning.
18. To consider any other academic matter concerning the Department of Artificial Intelligence and Machine Learning and make appropriate recommendations to the competent University authorities.