Use of Artificial Intelligence in Management Education: A Case Study of Moradabad

Use of Artificial Intelligence in Management Education: A Case Study of Moradabad

Dr. Sachin Bhardwaj, Harshit Saxena

Departmen of Management, MIT, Moradabad, India

Abstract -This paper explores how artificial intelligence (AI) is being used in management education in Moradabad, Uttar Pradesh, against the backdrop of state-level pushes for AI integration in universities. Using a single-case, mixed-methods design, the study maps AI applications in teachinglearning, assessment, and administrative processes in MBA/management programs located in Moradabad, and examines their perceived impact on student engagement, learning outcomes, and faculty workload.

Data are proposed to be collected through surveys, interviews, and document analysis from students, faculty, and academic leaders. Drawing on recent evidence from Indian higher education and B-schools, the paper anticipates benefits such as more interactive case discussions, personalized feedback, and early-warning analytics, alongside challenges like over-dependence on AI, assessment integrity, and uneven digital readiness.

The study concludes with a context-sensitive framework for responsible AI adoption in Moradabads management institutions, aligned with emerging Uttar Pradesh AI roadmaps and national higher-education priorities.

INTRODUCTION

Management education in India is being reshaped by two converging forces: the need to prepare leaders for an AI-driven economy, and the rapid availability of generative AI tools that can augment teaching, learning, and administration. Top B-schools have already begun redesigning

MBA programs to embed AI across core subjects,moving from standalone electives to integrated, practice-oriented curricula. At the same time, state governments are encouraging universities to adopt AI not only as a subject but as a cross-cutting capability in pedagogy, research and governance.

Uttar Pradesh, where Moradabad is located, has recently announced plans to embed AI across university curricula, research, and administration, including the development of

time-bound AI roadmaps and steering groups to drive implementation. This policy momentum creates a strategic window for management institutions in tier-2 and tier-3 cities like Moradabad to experiment with AI in ways that are affordable, context-appropriate, and aligned with local industry needs (e.g., MSMEs, trading, banking, and emerging IT/ITES roles).

However, evidence on how AI is actually being used in management programs outside metro B-schools remains limited. Many institutions face constraints such as infrastructure gaps, varying faculty readiness, and unclear policies on academic integrity and AI use. A grounded, local case study can help bridge this gap by documenting real practices, perceived impacts, and implementation challenges in a specific urban context.[ey][linkedin]

This study therefore focuses on management education institutions in Moradabad offering MBA/PGDM or equivalent programs. It seeks to understand:

How AI tools are currently being used (or planned) in teaching, assessment, and administration.

• How students and faculty

• perceive the impact of AI on engagement, learning, and workload.

• What barriers and ethical concerns arise in this context.

• What design and policy measures can support responsible, effective AI integration in Moradabads management programs.

  By situating the analysis within the broader Uttar Pradesh and Indian higher-education landscape, the paper aims to offer insights that are both locally relevant and transferable to similar cities.

  Objectives of the study

• To map current and planned uses of AI in management education institutions in Moradabad.

• To examine perceived impacts of AI on student engagement, learning outcomes, and faculty workload.

• To identify challenges, risks, and ethical concerns associated with AI adoption.

• To propose a context-sensitive framework for responsible AI integration in Moradabad management programme

  1. LITERATURE REVIEW

     1. AI in Indian higher education: policy and adoption trends

        Recent surveys of Indian higher-education institutions (HEIs) show growing AI adoption, with a strong emphasis on responsible and strategic use. The FICCIEYP AI Adoption Survey 2025 reports that a majority of participating HEIs already have or are developing institutional AI policies, indicating a shift from ad-hoc experimentation to more structured governance. At the same time, government-backed digital learning platforms now offer hundreds of free AI courses, treating AI upskilling as public digital infrastructure rather than a premium add-on.

        In Uttar Pradesh specifically, the state has launched initiatives such as AI Manthan 2.0, calling for comprehensive AI integration across university subjects, research, healthcare, agriculture, and administration. Universities are being urged to prepare clear, time-bound AI roadmaps, supported by an AI Policy Advisory Group and an AI Implementation Group. This policy environment creates both pressure and support for institutions in cities like Moradabad

        to begin formalizing their AI strategies.

     2. AI in management education and MBA programs

        Globally and in India, business schools are rethinking MBA curricula to produce AI-ready managers. Rather than isolating AI in one course, leading institutions are embedding it into finance, marketing, operations, and strategy through case-based discussions, action-learning projects, and capstones. Examples include

        • IIM Ahmedabads blended MBA in Business Analytics & AI, which integrates management, analytics, and AI through electives such as Human-AI Collaboration, AI Ethics, and Gen AI/Agentic AI.

        • IIM Sirmaurs AI-focused MBA for working professionals, which combines live online sessions with campus immersions, real-world simulations, and access to industry AI tools.

        • IIM Sambalpurs partnership to deliver AI-powered digital case studies, enabling faculty to shift from lecturers to active-learning facilitators.

          These initiatives highlight a broader trend: AI is being used not only as content to teach but as a pedagogical medium to enhance case discussions, simulations, and project work.

     3. AI-enabled teaching, assessment, and administration

        Research and practice point to three main clusters of AI use in management education:

        • Teachinglearning augmentation AI supports interactive case studies, scenario generation, role-play bots (e.g., CFO, customer), and micro learning modules that break complex topics into short, focused activities. Such approaches can increase engagement, personalize learning paths, and provide just-in-time explanations.

        • Assessment and feedback

          Adaptive quizzes, automated formative feedback on drafts, and AI-assisted rubric design help students identify gaps early and practice iteratively. However, these benefits depend on careful assessment redesign to avoid superficial, AI-generated submissions.[ey][linkedin]

        • Administrative and governance support

          AI dashboards for attendance, performance analytics, and early-warning systems can improve decision-making for academic leaders and reduce administrative burden. Case studies from Delhi-NCR HEIs report improved efficiency in administrative tasks and better planning accuracy through AI-supported analytics.

     4. Challenges and ethical considerations

        Despite clear potential, the literature also flags significant risks:

        • Over-dependence and shallow learning: Students may rely on AI to generate answers without developing deep understanding, especially when assessments are not AI-resistant.

        • Academic integrity: Unclear boundaries between acceptable AI assistance and misconduct create confusion for both students and faculty.

        • Digital readiness gaps: Variations in infrastructure, device access, and digital literacy can lead to unequal benefits, particularly in tier-2/3 cities.

        • Data privacy and governance: Uploading student data or work to external AI platforms raises compliance and ethical questions, especially in the absence of clear institutional policies.

          These challenges underscore the need for context-sensitive frameworks that combine pedagogical redesign, faculty development, student orientation, and clear governance.

     5. Gap addressed by this study

        • Faculty: Teaching faculty involved in curriculum delivery, assessment, or AI-related initiatives.

        • Administrators: Program heads, principals, or coordinators engaged in academic planning, IT, or quality assurance.

          Sampling will be purposive, selecting participants based on their direct involvement with AI-related activities or decision-making.

          3.3 Data collection methods

          Three primary sources of data will be used:

          1. Survey questionnaire

             While there is growing evidence from premier B-schools and metropolitan HEIs, there is limited empirical work on AI adoption in management programs in tier-2/3 cities like Moradabad. This study contributes by providing a grounded case analysis that links local practices and constraints with state-level AI roadmaps and national trends, offering actionable insights for similar contexts. These initiatives highlight a broader trend: AI is being used not only as content to teach but as a pedagogical medium to enhance case discussions, simulations, and project work.

  2. METHODOLOGY

     1. Research approach

        This study adopts a qualitative-dominant mixed-methods single-case design, with Moradabads management education ecosystem as the case. The approach is suitable because the research seeks an in-depth understanding of AI use within a bounded geographic and institutional context, while also capturing patterns across multiple stakeholders (students, faculty, administrators).

     2. Setting and participants

        Setting: Management institutions in Moradabad offering MBA/PGDM or equivalent programs (both private and affiliated colleges).

        Participants:

        • Students: Currently enrolled MBA/PGDM students who have experienced AI-enabled or AI-discussed courses.

          1. Target: Students and faculty across selected Moradabad management institutions.

          2. Content: Extent and types of AI tool usage (generative AI, adaptive quizzes, analytics dashboards, etc.).

             Perceived impact on engagement, understanding, grades, and workload (Likert-scale items).

        • Concerns about dependency, integrity, privacy, and equity.

        • A few open-ended questions for examples and suggestions.

          1. Semi-structured interviews

             1. Target: Approximately 1015 students, 68 faculty, and 35 administrators.

             2. Focus: Concrete examples of AI use in classes, assignments, and admin processes.

                Perceived benefits, challenges, and unintended consequences.

        • Views on policies, training needs, and future plans.

        • Duration: 2040 minutes each; audio-recorded (with consent) and transcribed.

          1. Document and artifact analysis

             1. Course outlines, assignment briefs, internal memos on AI use, IT/academic policies, and any available reports on digital initiatives.

             2. Where available, samples of AI-generated materials (case prompts, quiz items, feedback templates) will be examined to understand design and usage patterns.

             3. Data collection is proposed over one academic term/semester to capture initial implementation and early adjustments.

          3.4 Data analysis

          • Quantitative survey data: Descriptive statistics (frequencies, percentages, means) to profile AI usage patterns and perceptions; simple cross-tabs by institution type, specialization, or year if sample size permits.

          • Qualitative data (interviews + open-ended responses + documents): Thematic analysis using an initial codebook derived from the literature (e.g., engagement, personalization, workload, dependency, integrity, infrastructure, policy). Codes will be refined iteratively as new themes specific to Moradabad emerge.

          • Integration: Quantitative patterns will be used to contextualize and illustrate qualitative themes, creating a coherent narrative of how AI is experienced and governed in Moradabads management programs.

            Ethical considerations will include informed consent, anonymity, voluntary participation, secure data storage, and clear communication that participation will not affect grades or employment.

  3. RESEARCH DESIGN

     The research design can be summarized as follows:

     Type: Single-case, embedded mixed-methods design (case

     = Moradabad management education; embedded units = students, faculty, administrators, and courses).

     Time horizon: Cross-sectional with a short longitudinal element (data collected at two points in a semester/academic year to observe changes).

     Units of analysis:

     • AI-enabled courses and activities in MBA/management programs.

     • Perceptions and experiences of students and faculty.

     • Institutional documents, policies, and plans related to AI.

     Conceptual framework (narrative):

     AI integration in Moradabads management education is examined through three interrelated lenses:

     Pedagogical augmentation: AI as a tool to enhance active learning, case-based discussion, personalization, and feedback.

     Operational and governance support: AI for administrative efficiency, analytics-driven decision-making, and early-warning systems.

     Ethical and policy layer: Institutioal policies, norms, and capabilities that shape responsible and equitable AI use, aligned with UPs emerging AI roadmaps.

     This framework guides both data collection (what to ask/observe) and analysis (how to interpret findings in relation to policy and practice).

  4. ANALYSIS AND INTERPRETATION

     (Note: The patterns below reflect likely scenarios based on current Indian HE and B-school evidence. Replace illustrative figures with your actual survey/interview results when you conduct the study.)

     1. Current and planned AI use in Moradabad management programs

        Based on the proposed data collection, AI use in Moradabad can be expected to fall into three broad categories:

        Teachinglearning enhancement

        • Faculty in more digitally advanced colleges may use generative AI to create case variants, discussion prompts, and role-play characters (e.g., bank manager, MSME owner, HR head) tailored to local business contexts.

        • AI-generated summaries, concept explainers, and micro-videos may be shared via LMS or WhatsApp

          groups to support revision in subjects like financial management, marketing analytics, and operations.

        • Some programs may experiment with AI-assisted simulations for decision-making in marketing mix, inventory management, or financial planning, reflecting trends seen in premier B-schools.

          Assessment and feedback

        • Adaptive quizzes and auto-graded MCQs may be used in foundation courses (quantitative methods, business statistics, basics of AI/IT) to provide instant feedback and allow repeated practice.

        • Faculty may use AI to draft rubrics, check structure/clarity of reports, and generate formative comments, while retaining final grading authority.

        • In institutions aligning with UPs AI roadmap, there may be explicit discussions on acceptable AI assistance in assignments and projects.

          Administrative and governance support

        • Basic analytics dashboards for attendance, internal assessment marks, and submission tracking may be used by program coordinators to monitor performance and identify at-risk students.

        • Chatbots or automated messaging may handle routine queries about timetables, exam forms, and fee deadlines, reducing repetitive administrative workload.

        • Leadership teams may begin exploring AI-supported planning tools for enrollment forecasting, resource allocation, and accreditation documentation, in line with broader UP HE initiatives.

     2. Perceived impact on engagement and learning

        Students are likely to report higher engagement in courses where AI:

        • Makes case discussions more interactive and locally relevant (e.g., cases on Moradabads brass industry, trading clusters, or local fintech/banking scenarios).

        • Provides immediate feedback on quizzes and drafts, helping them identify weak areas before exams.

        • Offers multiple explanations or examples when they struggle with quantitative or technical topics.

          Many may describe feeling more confident tackling projects and presentations because they can test ideas with AI before submitting. Faculty who actively integrate AI often note that well-designed activities encourage deeper preparation for class, as students come with AI-informed questions rather than only surface summaries.

          Quantitatively, a typical pattern (based on Indian HE surveys) might show a majority of respondents agreeing or strongly agreeing with statements such as:

        • AI tools make learning more interactive.

        • AI helps me understand difficult concepts.

        • AI-based quizzes help me identify my weak areas.

          However, responses may vary by institution type, infrastructure quality, and faculty training levels.

     3. Impact on faculty workload and teaching practice

        Faculty responses are likely to show a mixed picture:

        • Workload reduction in routine tasks: AI can help generate first drafts of cases, quiz items, and feedback comments, saving preparation time.

        • Increased design effort initially: Redesigning assignments to be AI-resistant and pedagogically meaningful requires upfront investment, especially for faculty new to these tools.

        • Shift in role: Faculty may describe moving from content deliverer to learning designer and coach, focusing more on facilitating discussions, critiquing AI outputs, and guiding ethical use.

        • Those who receive training and institutional support are likely to view AI more positively and report greater confidence in integrating it responsibly.

     4. Challenges and ethical concerns specific to Moradabad

        Despite potential benefits, several challenges are likely to emerge:

        Over-dependence and superficial learning

        Some students may admit using AI to generate large parts of assignments with minimal personal input, especially when assessments are not carefully designed. This mirrors concerns in the broader literature about cognitive dependency and shallow engagement.

        Academic integrity and assessment design

        Unclear boundaries between acceptable AI assistance and misconduct may create confusion. Faculty may express concern that traditional take-home assignments are easy to outsource to AI, pushing them to redesign assessments toward in-class work, oral defenses, and process-based evaluation.

        Infrastructure and digital readiness gaps

        Variations in internet quality, device access, and digital literacy among students and faculty may lead to uneven benefits. Institutions with better infrastructure and training programs are likely to report more positive experiences, while others may struggle with basic adoption.

        Policy and governance gaps

        In the absence of clear institutional AI policies, faculty and students may be uncertain about what is allowed. This is particularly relevant as UP moves toward state-wide AI roadmaps; local institutions will need to translate high-level directives into concrete guidelines for classrooms and assessments.

        Data privacy and vendor dependence

        Uploading student work or personal data to external AI platforms raises privacy and compliance questions, especially if institutions lack formal agreements or data-protection protocols.

     5. A context-sensitive framework for AI integration in Moradabad

        Based on the anticipated findings and existing literature, a practical framework for Moradabads management institutions could

        include:

        • Clear, localized AI policies: Define acceptable and unacceptable uses of AI for different assignment types; specify disclosure requirements

          when AI is used; align with UPs emerging AI roadmaps.

        • Assessment redesign: Emphasize process, reflection, and oral components; use AI-resistant tasks such as in-class problem-solving, vivas, local-industry projects, and project logs.

        • Faculty development: Offer workshops on AI pedagogy, prompt design, ethics, andassessment strategies; create communities of practice for sharing AI-enabled lesson plans and local case ideas.

        • Student orientation: Educate students on responsible AI use, critical evaluation of AI outputs, and academic integrity expectations; integrate short modules on AI for managers across core courses.

        • Human-in-the-loop governance: Ensure that key decisions (grading, interventions, policy enforcement) remain under human oversight, with AI as a support tool rather than a decision-maker.

        • Leveraging local context: Encourage AI-supported projects on Moradabad-specific themes (MSME digitization, local supply chains, retail analytics, banking/fintech adoption) to make learning directly relevant to the regional economy.

          This framework positions AI as an augmentative resource that strengthens, rather than replaces, core elements of management education: critical thinking, ethical judgment, contextual understanding, and interpersonal skills.

  5. CONCLUSION

This study highlights that artificial intelligence can play a meaningful role in transforming management education in Moradabad, but only when its adoption is purposeful, context-sensitive, and ethically grounded..

The evidence from Indian higher education and leading B-schools suggests that AI can enrich case-based learning, personalize feedback, and support data-driven academic decision-making. At the same time, the Moradabad contextcharacterized by diverse institutional capacities, varying digital readiness, and emerging state-level AI directivesdemands careful design rather than uncritical adoption.

The proposed frameworkcombining clear institutional policies, assessment redesign, faculty and student capacity building, and human-in-the-loop governanceoffers a practical roadmap for management institutions in Moradabad to move from ad-hoc AI experimentation to structured, responsible integration. By anchoring AI use in

local business realities (such as MSMEs, trading clusters, and regional banking/fintech ecosystems), management programs can ensure that AI serves not only as a technological upgrade but as a lever for more relevant, applied, and future-ready management learning.

References

1. EY India. (2025). AI in higher education: 2025 survey & latest insights.

   https://www.ey.com/en_in/insights/education/harnessing-ai- in-higher-education-opportunities-and-the-road-ahead[ey]

2. Jagran Josh. (2026, September 15). UP universities to get AI roadmaps: Governor Anandiben Patel pushes skill-based learning. https://www.jagranjosh.com/news/up-universities- to-get-ai-roadmaps-governor-anandiben-patel-pushes-skill- based-learning-187437

3. The Economic Times. (2026, June 16). From MBA to AI-powered leadership: Indias top B-schools redesign programmes for an AI-driven economy. https://education.economictimes.indiatimes.com/news/highe r-education/from-mba-to-ai-powered-leadership-indias-top- b-schools-redesign-programmes-for-an-ai-driven- economy/131774896[education.economictimes.indiatimes]

4. The 420. (2026, September 14). Uttar Pradesh moves to embed AI across universities, research and administration. https://the420.in/uttar-pradesh-universities-ai-policy- research-

   Administration/

5. Channel Life. (2025, November 21). IIM Sirmaur launches AI-focused MBA with INR 8,00,000 fee. https://channellife.in/story/iim-sirmaur-launches-ai-focused- mba-with-inr-8-00-000-fee

6. Bellary, S. (2025). Generative artificial intelligence for management education: Applications, benefits, challenges and future research directions. International Journal of Educational Management, 39(5), 12171239. https://doi.org/10.1108/IJEM-xx-xxxx[rsisinternational

   QUESTIONNAIRE

   Instructions: Please respond based on your experience in your MBA/management program in Moradabad. Use the scale:

   1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.

   For Q7, write in your own words.

   1. AI tools (such as chatbots, generative AI, adaptive quizzes, or analytics dashboards) are regularly used in my management courses.

      1 2 3 4 5

   2. The use of AI in my courses has made learning more interactive and engaging.

      1 2 3 4 5

   3. AI-based explanations, summaries, or feedback have helped me understand difficult management concepts more clearly.

      1 2 3 4 5

   4. I am concerned that students (including myself) may become over-dependent on AI for completing assignments and projects.

      1 2 3 4 5

   5. My institution has clear guidelines on what is and is not allowed when using AI for academic work.

      1 2 3 4 5

   6. Infrastructure (internet, devices, platforms) and training are adequate for effective and responsible use of AI in my program.

      1 2 3 4 5

   7. In your own words, describe one way AI has positively or negatively affected your learning experience in your management program, and suggest one improvement your institution could make regarding AI use.

AUTHORS

1. Dr. Sachin Bhardwaj serves as an Associate Professor in the Management Department at the Moradabad Institute of Technology (MIT), Uttar Pradesh. With a deep expertise in Marketing management and Business Ethics, he actively contributes to management education through impactful teaching and academic research. His scholarly work includes various published research papers focusing on corporate social responsibility, contemporary financial systems, and ethical business management. Committed to academic excellence, he guides MBA students in developing analytical skills and strong professional values. His academic achievements and professional background are well-documented on platforms like Google Scholar and LinkedIn.

2. Harshit Saxena is an MBA student specializing in Finance and Human Resources, with a Bachelor of Technology in Computer Science and Engineering. His academic and research interests include artificial intelligence

in management, fintech, business analytics, machine learning applications, and emerging digital business models. He has worked on research and academic projects related to AI in management education, fraud detection using machine learning, and business applications of NFTs. With a background combining technology and management, he aims to explore data-driven solutions for real-world business and financial challenges. His work reflects a strong interest in the practical application of AI and analytics in business decision-making.