In an era when data is abundant and time is at a premium, innovative tools like Deep Research are reshaping how we approach complex analysis and content creation.
Last month, OpenAI introduced Deep Research—a cutting-edge feature currently available for all ChatGPT paid users. Designed to perform advanced searches and reason across diverse datasets, Deep Research offers a new way to generate literature reviews, discussion papers, technical insights, and even newsletters.
In this post, I’ll explore how Deep Research can serve as a powerful ally for MIT Sloan faculty members, staff, and teaching assistants who are engaged in the creation of learning materials, along with some pitfalls to consider when using these tools. By unpacking a detailed case study example on “Expectations Management and the Earnings Game,” I’ll consider both the tool’s potential and its practical considerations.
Introducing Deep Research
Deep Research represents a significant leap in the integration of AI within academic and professional research workflows. At its core, this tool combines extensive data retrieval with robust analytical reasoning, allowing users to generate comprehensive content in a fraction of the time traditional methods would require.
Key Capabilities
- In-Depth Analysis: From literature reviews to deep insights on technical, analytical, or historical queries, Deep Research harnesses publicly available information to produce detailed content.
- Structured Output: The tool organizes information as requested—whether you ask for an introduction, a developed argument, or a conclusive synthesis it provides a tailored result.
- Source Transparency: In most cases it provides direct links to the exact excerpts used in forming its responses, enhancing traceability and facilitating quick verification.
- Time Efficiency: Imagine condensing a day’s worth of research into just a few minutes. In our highlighted example, Deep Research produced a 16-page case study in only 6 minutes, referencing 22 sources including NYU, Duke, SEC, Reuters and Columbia.
A Closer Look: The Case Study on “Expectations Management and the Earnings Game”
Here’s how I used Deep Research to generate a starting point for an educational case study on corporate earnings management.
The Prompt
I started by providing the following prompt to Deep Research:
Write a case study on Expectations Management and the Earnings Game. Begin with a compelling and vivid narrative that introduces the key conflict or dilemma, engaging the reader with a real-world scenario. The first few pages should tell a story that sets the stage for the analytical discussion. After this introduction, provide a structured analysis that includes historical background, key actors, incentives, relevant regulations, and the consequences of the case. Incorporate deep research, financial data, regulatory excerpts, and impact analysis. The case study should follow a logical structure with an engaging introduction (story-driven), followed by an in-depth technical and analytical exploration of the topic, using academic sources and relevant examples.
The Interactive Process
What makes Deep Research valuable is its interactive approach. Before generating the full case study, it asked these clarifying questions:
- Do you have a specific company or real-world example in mind for the story-driven introduction, or should I select one?
- Are you looking for a particular industry focus (e.g., tech, finance, pharmaceuticals)?
- Should the financial data be recent (last 5-10 years), or would historical cases be acceptable?
- Do you have any preferred academic sources or regulatory frameworks to reference?
- Should the case study be formatted in a particular academic style (e.g., APA, Harvard)?
I responded with the following: “Choose whatever company or real-world example you think is the best. The same for the industry. Use historical and recent cases. Both academic and regulatory frameworks. Use APA, please.”
The Outcome
Deep Research delivered an extensive 16-page document titled “Expectations Management and the Earnings Game” within 6 minutes. This raw output included historical cases, recent examples, regulatory landscape, legal cases and their consequences. The document also referenced 22 sources.
I captured the working process in this video demonstration:
Here’s the entire case study draft that Deep Research generated in response to my prompts: Deep Research Case Study Draft- Expectations Management and the Earnings Game.
This exercise demonstrated how AI can efficiently generate a solid starting point for academic content development—though as with any AI output, I’d consider this a foundation to build upon rather than a finished product.
Comparing to an Existing Case Study
To better understand the strengths and limitations of this AI-generated content, I compared it with a case study by the same title that I developed in collaboration with Cate Reavis, MIT Sloan’s former associate director of curriculum development. While that case study is not yet published, the side-by-side analysis provided valuable insights into what would be needed to transform the AI draft into classroom-ready material.
Thorough fact-checking would be a critical first step for any faculty member working with this AI-generated content. While I haven’t verified whether the Deep Research case contains inaccuracies, fact-checking is an essential quality control measure for any teaching material, especially when using AI-generated content as a foundation.
The case we’d developed here at MIT Sloan included visual aids, discussion questions, and data exhibits that enhanced the learning experience. Those were all absent in the AI version. These elements, while valuable hallmarks of MIT Sloan cases, might not be essential in every classroom context.
Another important distinction was that the MIT Sloan case gave me, a faculty member, the opportunity to select and contextualize real-world examples. Deep Research’s raw output provides a starting point, but an expert’s knowledge makes the difference between basic information and truly effective teaching material that gives students accurate insights into corporate financial practices.
How Deep Research Works: Process and Practical Experience
Using Deep Research is a blend of high-tech automation and a measured, research-informed approach. Here’s a closer look at the process and the practical takeaways for faculty and academic developers:
Guided Querying for Enhanced Precision
Rather than simply generating content from a static database, Deep Research actively engages with the user. The initial prompt is followed by a series of clarifying questions, aiming that the final output is aligned with the specific structure and characteristics requirements of the task. This guided querying is particularly beneficial when developing case studies that require detailed perspectives and precise data points.
Efficient Data Collation and Verification
One of the recurring challenges in academic research is the time-intensive process of data collation and source verification. Deep Research mitigates this challenge by:
- Rapid Information Retrieval: Delivering a fully fleshed-out case study in a matter of minutes.
- Source Transparency: Including direct links to the exact excerpts used, so that researchers can quickly verify the information.
- Facilitating a Starting Point: While the output is comprehensive, it serves as a robust starting point rather than a final product. Faculty members can then integrate additional elements such as graphics, tables, and custom formatting to align with specific course materials.
Balancing Innovation with Rigor
It’s important to note that despite its impressive capabilities, Deep Research is not infallible. The tool may occasionally generate content that requires further verification or additional contextual refinement. As such, while it can save significant time in the initial research phase, the final content should be carefully reviewed by subject matter experts. This iterative process—leveraging AI for initial drafts and applying human expertise for refinement—ensures both efficiency and academic rigor. For example, here are some hallucinations I identified in a different task:
A recent study by Kumar and colleagues (2024) explored the impact of repeated large-language model usage on human creative thinking. Their findings suggest that while using the AI improved performance during assisted brainstorming (people produced more ideas with the AI’s help), it also led to less originality when those same people later tried to think without the AI.
If this finding were real, it would imply that heavy AI use for creative tasks could diminish independent creative “fitness” over time—unless counterbalanced by unassisted practice. However, no such study by Kumar and colleagues (2024) exists. In fact, no known study supports the claim made in the AI-generated text. Yet, the argument is presented in a convincing and credible manner, demonstrating how misleading AI hallucinations can be.
Another type of hallucination, which doesn’t include even the appearance of a citation, appeared in the same task where the Kumar and colleagues (2024) reference was mentioned:
As one researcher argues, previous tools “did not solve the problem for you; they assisted with part of the problem and provided information that you had to integrate into a plan or decision-making process.”
The lack of a citation makes this hallucination easier to identify. However, both cases serve as a reminder: It’s important to always review outputs from Deep Research for accuracy.
Advantages and Considerations for MIT Sloan Faculty
For MIT Sloan faculty members, teaching assistants, and staff involved in developing learning materials, the implications of integrating Deep Research into your workflow are worth considering.
Time-Saving Efficiency
Deep Research’s ability to condense extensive research into a digestible format can dramatically reduce preparation time. In our example, the tool accomplished what might otherwise take hours of manual work in just a few minutes. Even though it won’t eliminate the entire time spent on creating materials—since fact-checking sources is still necessary—This efficiency enables educators to allocate more time to refining pedagogical strategies and enhancing the overall learning experience.
Enhanced Research Depth
By pulling from a broad array of reputable sources, Deep Research generate content is not only thorough but also includes trackable references. This deep research capability is particularly valuable when preparing case studies or discussion materials that demand rigorous academic backing. The tool’s inclusion of direct source links further bolsters its utility, as it allows for quick cross-referencing and validation—a critical aspect when supporting research claims with evidence.
Customizability and Adaptability
While the output from Deep Research is comprehensive, I find it best serves as a foundation upon which more tailored content can be built. Faculty members can take the generated draft and modify it to include additional elements such as:
- Visual Aids: Integrating custom graphs, charts, or tables.
- Contextual Enhancements: Adding specific examples or commentary that align with the unique perspectives of MIT Sloan.
- Formatting Adjustments: Adapting the structure to match existing teaching materials or preferred stylistic guidelines.
Considerations for Implementation
Despite its many advantages, users must remain mindful of certain limitations:
- Reliance on Public Data: Deep Research exclusively accesses publicly available documents. Any research requiring proprietary data or analysis behind paywalls will necessitate supplementary sourcing.
- Need for Critical Review: While the tool rarely produces factual inaccuracies, occasional hallucinations or misinterpretations can occur. It remains imperative for faculty to verify the information before incorporating it into finalized teaching materials.
- Not a Complete End Product: As with many AI-generated drafts, the output from Deep Research is a starting point. Additional layers of analysis, formatting, and contextual commentary are often required to fully align with the rigorous standards of academic materials.
Concluding Thoughts and Future Perspectives
Deep Research represents an exciting convergence of artificial intelligence and academic research—a tool that can significantly enhance the efficiency and depth of case study development. For MIT Sloan faculty and staff, it offers a transformative way to approach the initial phases of content creation, allowing more time for refinement and pedagogical innovation.
By automating the labor-intensive aspects of research, Deep Research empowers educators to focus on what matters most: creating engaging, insightful, and rigorously supported learning materials. As the tool continues to evolve and potentially becomes available to a broader user base, its role in reshaping academic workflows is only set to expand.
In the end, while Deep Research provides a powerful starting point, the collaboration between AI and human expertise remains key. Faculty members are encouraged to integrate this tool into their workflows—leveraging its efficiency while applying their critical judgment to ensure the highest standards of academic integrity.
I invite you to watch the video demonstration and explore the Deep Research-created case study draft. As always, your insights and experiences with integrating AI tools into academic work are invaluable. I look forward to hearing how Deep Research transforms your approach to crafting learning materials.
By exploring innovative tools like Deep Research, faculty at MIT Sloan can continue to lead the way in integrating cutting-edge technology with evidence-based teaching practices, ensuring that our academic community remains at the forefront of educational excellence. As I continue to develop my own case studies, I’m excited to see how these tools might complement our traditional research and writing processes.
