Key insight: AI accelerates the shift from rote learning to applied, personalized problem-solving—but grading and truth-checking now demand more from both students and educators.
The Evolution of Learning in MBA Programs
MBA education has always mirrored the demands of the real world—shifting from memorization and concept mastery to handling ambiguity, partial access to information, competing priorities, constrained resources, and personalized goals. The rise of AI isn’t replacing that—it’s amplifying it.
Tools like ChatGPT, Claude, or Perplexity have become part of the modern MBA toolkit. At Hult International Business School, we received an AI Policy for our school usage that helped students synthesize information faster, communicate with more clarity, and frame their ideas with precision. But they also change the expectations of what it means to “do the work” as a student.
The acceleration of the first draft is only that: a draft. The bottleneck in my experience was how effectively teams communicated and aligned on priorities, framing, and strategy in terms of how a problem was to be solved.
Benefits for the Student
1. Personalized learning
AI allows students to rephrase, challenge, or simplify complex readings in real time. This has been an incredible help for learners coming from diverse educational and professional backgrounds. It allowed me to study at my level—whether I needed context, critique, or an industry-specific translation of academic concepts. Personally coming from an engineering background, the use of AI really helped me accelerate my learning in terms of new fields such as accounting, finance, operations, and contexts that require multi-dimensional considerations such as climate change, energy, and ESG.
2. Applied reasoning, not rote recall
With AI handling the low-level cognitive load (summarization, outlining, translation), students can focus on the higher-value skills:
- Framing arguments
- Comparing trade-offs
- Writing with audience awareness
- Making contextual decisions under uncertainty
3. Acceleration of idea-to-output
In group projects, solo reflections, or case study writeups, students can now produce more structured drafts faster. However, the acceleration of the first draft is only that: a draft. The bottleneck in my experience was how effectively teams communicated and aligned on priorities, framing, and strategy in terms of how a problem was to be solved. Typical stakeholder alignment.
Benefits for the Educator
1. Seeing how students think
AI-generated drafts reveal a student’s instincts, prompts, and edits. Professors now witness not just the final output, but how students reason. With the right systems in place, this can give educators a new lens into cognitive growth.
2. Emphasis on critical judgment
In a world where anyone can generate a decent summary, what matters is discernment:
- What does the student choose to highlight or omit?
- How well do they contextualize the theory?
- Can they apply it to their personal goals or professional domain?
3. Opportunity to redesign assessments
The presence of AI forces a rethink: multiple-choice tests or surface-level essays no longer suffice. Instead, one angle that I am considering that may be new opportunities to evaluate students is how they progress in the quality of questions they ask as they explore a new problem in terms of speed, strategy, and precision in problem solving. Assessments must reward synthesis, original framing, and audience alignment—skills that machines can scaffold but not own.
Limitations of AI
1. Truth vs fluency
AI can be persuasive and wrong. It often hallucinates sources, misquotes papers, or offers false certainty. Students must learn how to verify before they trust.
2. Blurring of authorship
Who really wrote this: the student, the AI, or a hybrid? This gray area complicates grading, especially in programs that rely heavily on narrative and reflective assignments.
3. Flattened learning if misused
Over-reliance on AI leads to shallow thinking. If a student uses it to replace effort instead of amplifying it, the long-term cost is intellectual laziness dressed up as insight.
Closing Thoughts: From Content to Context
AI tools certainly accelerate the generation of content. However, it is a mistake to believe that the value of an MBA program is “breadth of frameworks that you become familiar with” and “the time it takes to generate an executive summary” or “time to first draft”. Value is created in the timeliness of delivering a strategy or critical analysis that translates to swifter decision-making in an environment with constrained resources. There are many frameworks out in the world but the critical skill that students need to develop is knowing how to specifically define and frame the problem, prioritize risk and opportunities, weigh different options, and ask the right questions. The work is in critical thinking, and generating tokens cannot be used as a proxy of progress.
Movement is not progress.
Educators will need to redesign learning environments that reward curiosity and precision. Students will need to develop stronger filters for truth and relevance and both sides will need to get comfortable with co-creating knowledge. In the same way that artists use large paint brushes to accelerate the broad strokes and finer paint brushes for important details, I believe that AI tools have a place in the domain of writing.
Foolishness is thinking that LLMs will completely replace writing because “writing manually picking the words is slow”. Learning takes time. As a lifelong learner, that is how we relinquish the ability to synthesize ones’ own ideas by giving up the exercise.
