A 4-step approach on using generative AI support students’ learning to do dissertation projects: for part-time MBA students

A 4-step approach on using generative AI support students’ learning to do dissertation projects: for part-time MBA students

 

In the context of "part-time MBA students learning to do dissertation projects", could you suggest 2 academic theories from the field of management education and 2 academic theories from the field of adult education that can be used to study and advice (to students) on this topic?

Here are four theories that fit your context and can both guide research and inform practical advice to part-time MBA students doing dissertations. Each one can be framed into research questions, constructs, and advisory implications.

From management education

1. Experiential Learning Theory (Kolb)

Kolb’s experiential learning theory views learning as a cycle: concrete experience, reflective observation, abstract conceptualization, and active experimentation. This is widely used in management education to explain how professionals learn from work-based and project-based tasks, including dissertations that draw on their own organizations.

For your topic, you could:

·        Treat the dissertation as a “capstone experience” and study how part-time MBAs move through the cycle (e.g., workplace problem as experience, supervision as reflection, literature as abstraction, recommendations as experimentation).

·        Advise students to deliberately structure their dissertation process to include formal reflection (logs, journals), theory building, and testing recommendations back in their workplaces.

A possible research angle: “How do part-time MBA students engage in the experiential learning cycle during dissertation projects, and what factors facilitate or block each phase?”

2. Action Learning / Work-Based Learning

Action learning is widely applied in part-time MBAs, especially in final-year research modules and dissertations. It emphasizes real organizational problems, cycles of action and reflection, and learning with/through peers and facilitators.

For your topic, you could:

·        Use action learning theory to examine how dissertation projects that solve genuine workplace issues support both individual learning and organizational change.

·        Advise students (and schools) on using learning sets, peer feedback, and iterative “plan–act–review” cycles during the dissertation process.

A research angle: “In what ways does positioning the dissertation as an action-learning project influence engagement, perceived relevance, and completion among part-time MBA students?”

From adult education

3. Andragogy (Knowles’ Adult Learning Theory)

Andragogy is one of the most influential frameworks in adult education and focuses on adult learners’ self-concept, prior experience, readiness to learn, orientation to learning (problem-centred), and internal motivation.

For your topic, you could:

·        Use andragogical principles to analyze how dissertation design and supervision align (or misalign) with adults’ needs for autonomy, relevance to work, and use of experience.

·        Advise students to take a more self-directed role: negotiating topics that align with their practice, drawing systematically on their professional experience, and clarifying the immediate relevance of their research to their careers.

A research angle: “How do andragogical principles manifest in the way part-time MBA students select topics, interact with supervisors, and manage their dissertation learning?”

4. Self-Directed Learning (SDL)

Self-directed learning theory focuses on learners’ initiative in diagnosing needs, setting goals, identifying resources, choosing strategies, and evaluating outcomes. It has been a central theme in adult education research and is especially relevant when students juggle work, family, and study.

For your topic, you could:

·        Study the extent to which part-time MBA students exhibit SDL behaviours in managing their dissertation (e.g., planning timelines, seeking feedback, finding literature independently).

·        Advise students on building SDL capabilities: project planning, monitoring progress, using peer support, and seeking supervisory guidance proactively.

A research angle: “What self-directed learning strategies do successful part-time MBA students use to complete dissertation projects while working, and how do these strategies relate to perceived challenges and outcomes?”

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If you were to choose just two, a strong, coherent combination would be:

·        From management education: Experiential Learning and Action Learning

·        From adult education: Andragogy and Self-Directed Learning

Together, they give you:

·        A practical–theoretical lens on learning from work-based dissertation projects

·        A robust adult learning lens on autonomy, relevance, and self-management

How comfortable do you feel with these four theories so far, and which one do you think best fits the way you, personally, approached your own dissertation work?

Could you also suggest 1 method of student learning from the academic field on "how to make use of generative AI to support MBA students?"

A useful, researchable method you could use is a “generative-AI augmented case method” for MBA learning. This adapts the traditional case method but explicitly builds structured AI use into how students analyze, discuss, and reflect on cases.

In this method, students first read and analyze a case on their own (or in groups), then use a generative AI tool to:

·        simulate roles (e.g., “act as the CEO/consultant in this case”) and explore alternative decisions

·        generate structured analyses (e.g., draft a SWOT or Five Forces) that students must then critique, correct, and extend

·        rehearse presentations or arguments and then refine them based on AI feedback.

Pedagogically, the learning focus is on evaluating and improving AI outputs, not accepting them; students must compare their own reasoning with the AI’s, identify errors, and add deeper, context-specific insight. This aligns well with MBA goals of critical thinking and applied judgment, and could be adapted directly to dissertation supervision (e.g., AI-assisted literature mapping, proposal critique, or method selection, always with student critique and supervisor oversight).

If you were to build this into your dissertation study, you could frame it as: “How does a generative-AI augmented case/dissertation method influence part-time MBA students’ critical thinking, self-directed learning, and confidence in research tasks?”

From your perspective as a part-time MBA student, which dissertation stage do you feel would benefit most from this AI-augmented method: topic selection, literature review, methodology design, data analysis, or writing?

Please synthesize the 5 academic theories above (2 from the management education fields, 1 from adult education; 1 from generative AI supported learning) to produce a 4-step approach for "using generative AI to support part-time MBA students' learning in doing dissertation projects".

Here is a synthesized 4-step approach that integrates the five theories (Experiential Learning, Action Learning, Andragogy, Self-Directed Learning, and the Generative-AI Augmented Case Method) to guide part-time MBA students in using generative AI effectively for dissertation projects.

A 4-step approach on using generative AI for dissertation learning

Step 1: Anchor the dissertation in a real work problem (Andragogy + Action Learning)

Begin by having students define a clear, work-based problem that matters to their role or organization, and explain why it is relevant to them as experienced professionals. This aligns with andragogy (relevance, use of experience, problem-centred orientation) and action learning (learning through real issues and action cycles).

Students then use generative AI to:

·        Clarify and refine their initial problem statement (e.g., “Help me sharpen this problem statement into a clear, researchable question for an MBA dissertation”).

·        Surface assumptions and stakeholders they may have missed, and draft a simple problem–context–impact map.

The key is that AI supports problem framing, but the student remains the owner of the problem and its relevance to their practice.

Step 2: Plan the dissertation as a self-directed learning project (SDL + Experiential Learning)

Treat the dissertation explicitly as a self-directed learning project: students diagnose their learning needs, set goals, identify resources, and plan how they will manage time and supervision. Combine this with experiential learning by mapping how each phase (experience, reflection, theory, experimentation) will be supported.

Students use generative AI to:

·        Draft a personalized project plan (timeline, milestones, risks) based on their work and family commitments.

·        Identify key competencies they need to develop (e.g., literature searching, basic statistics, academic writing) and suggest resources or micro-learning activities.

·        Propose a reflective journal structure (e.g., weekly prompts) to track what they did, what they learned, and what they will try next.

Students then review and adapt the AI-generated plan to fit their reality, reinforcing self-direction and responsibility.

Step 3: Use AI as a “critical partner” for literature, method, and analysis (AI-Augmented Case Method)

In this step, generative AI acts like a critical partner in an AI-augmented case method: it drafts, suggests, and simulates, while students evaluate, correct, and deepen. This operationalizes experiential learning (moving from experience to abstraction and back) and self-directed learning (proactively seeking and using resources).

Practical applications include:

·        Literature review: ask AI to summarize key debates, suggest search terms, or draft an annotated outline; students then verify sources, check citations, and add critical evaluation.

·        Methodology design: ask AI to compare suitable methods (e.g., case study vs. survey), outline sampling and data collection options, and list pros/cons; students refine these against their context and supervisor advice.

·        Data analysis and interpretation: for qualitative data, use AI to help generate initial codes or themes; for quantitative, use AI to explain output or draft interpretation sections; students must re-check logic, context, and fit with theory.

Students are explicitly trained to treat AI outputs as drafts to critique, not final answers, developing critical thinking and judgment.

Step 4: Embed action–reflection cycles and close the loop to practice (Action Learning + Experiential Learning)

Finally, structure the dissertation process as ongoing action–reflection cycles: students implement small actions (e.g., pilot interviews, trial analysis, draft chapter), reflect on what worked, and adjust their next steps. This mirrors action learning sets and experiential learning cycles, and it keeps the dissertation connected to real practice.

Students use generative AI to:

·        Help them structure reflective entries (e.g., “What did I try? What did I learn? What will I do differently?”) and extract patterns over time.

·        Draft “implications for practice” sections and recommendations, then refine them to be specific, feasible, and aligned with their organization’s context.

·        Simulate how different stakeholders (manager, team, client) might react to their recommendations, helping them anticipate resistance and refine their proposals.

Supervisors can encourage students to share selected AI-supported reflections or drafts in supervision meetings, focusing discussion on students’ critical choices and judgment rather than on mechanical editing.

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This 4-step approach gives you:

·        A clear, theory-grounded sequence (from problem framing to practice-oriented recommendations)

·        A way to use generative AI that is consistent with adult and management learning principles (relevance, self-direction, experience, critical thinking)

[Asking generative AI to offer relevant concepts and then to synthesize them into a theoretical framework or approach draft is also a kind literature review exercise.]

** also study a video on adult learning theories. 

A collection of blog notes on using chatgpt for research purpose.