{"id":4091,"date":"2025-09-01T09:04:54","date_gmt":"2025-09-01T08:04:54","guid":{"rendered":"https:\/\/microsites.ncl.ac.uk\/casestudies\/?p=4091"},"modified":"2025-09-02T16:24:44","modified_gmt":"2025-09-02T15:24:44","slug":"the-lov-ai-co-creation-approach-creating-business-teaching-cases-with-deep-research-ai","status":"publish","type":"post","link":"https:\/\/microsites.ncl.ac.uk\/casestudies\/2025\/09\/01\/the-lov-ai-co-creation-approach-creating-business-teaching-cases-with-deep-research-ai\/","title":{"rendered":"The LOV AI Co-Creation Approach: Creating Business Teaching Cases with Deep Research AI"},"content":{"rendered":"

Dr David Grundy, Senior Lecturer in Digital Education, (NUBS)<\/strong><\/h4>\n

Faculty of Humanities and Social Science<\/strong><\/h4>\n
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What did you do?<\/strong><\/p>\n

I introduced and applied the Lecturer Oversight and Verification of AI (LOV AI) Co-Creation Approach to develop business teaching cases for a Finance and Investment MBA module delivered in a one-week, block-mode format (21 contact hours plus six hours of guided online work). Between March and April 2025, I combined OpenAI\u2019s ChatGPT o3 Deep Research model with structured expert supervision to produce extended case studies, concise summaries, and a suite of derivative teaching materials.<\/p>\n

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Who is involved?<\/strong><\/p>\n

The module involved in this project was a Finance and Investment option module aimed at a general MBA audience taught in a case-led style. The module was delivered in a block-mode delivery pattern, with the students being taught 21 hours of primary contact time in a single week. The module had a further six hours of online supported guided learning materials which the students\u2019 needed to work through including case videos, case-related interactive games and practice questions.<\/p>\n

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How did you do it?<\/strong><\/p>\n

First, I employed ChatGPT o3 mini-high with Internet Search to surface candidate case topics that aligned precisely with our syllabus objectives, iteratively refining prompts\u2014and even excluding unsuitable companies\u2014to land on rich, well-documented business scenarios (Step 1). Next, I crafted a highly detailed \u201cdeep research\u201d prompt (\u2248800 words) instructing ChatGPT o3 to exhaustively generate narrative background, quantitative data, stakeholder analyses, and student discussion questions, leveraging its 200,000-token context window to produce over 20,000 words of raw case content and answer guides (Step 2).<\/p>\n

Then, in the Ownership Phase (Step 3), I thoroughly reviewed every output, cross-checking sources (e.g., Financial Times, Reuters), correcting interpretation errors, deleting tangential sections, and ensuring alignment with pedagogical standards. This critical verification\u2014covering roughly 10,000 words of case text and 10,000 words of answers\u2014was the most time-consuming but indispensable for academic rigor.<\/p>\n

Once verified, I distilled the extended draft into an 800\u20131,000-word student-ready case summary (Step 4). Building on this artefact, I then generated a range of supplementary resources\u2014interactive storytelling games via Gemini 2.5 Pro, an AI-driven chatbot supplemented by a narrated vodcast (ElevenLabs), PowerPoint decks, and Panopto recordings\u2014to support diverse learning activities (Step 5).<\/p>\n

To create decision-focused vignettes, I further prompted ChatGPT to reshape the extensive case into a 3\u20134-page plot-driven narrative following classical case-writing principles (McNair, May, Andrews) (Step 6).<\/p>\n

\"Diagram

Figure 1: The LOV AI Co-Creation Approach<\/p><\/div>\n

Overall, this co-creative process cut case development time from weeks to about four hours, while maintaining the depth, accuracy, and complexity expected at the MBA level.<\/p>\n

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Why did you do it?<\/strong><\/p>\n

I did this because writing deep, nuanced MBA teaching cases is traditionally so time-consuming\u2014and often prohibitively expensive\u2014that many instructors simply buy ready-made cases rather than author their own. I wanted to explore whether the latest generative AI (OpenAI\u2019s ChatGPT o3 Deep Research) could serve as a genuine co-creator\u2014accelerating case development from weeks to hours\u2014while keeping pedagogical rigour intact. By embedding a structured Lecturer Oversight and Verification (LOV AI) approach, I ensured all AI outputs were fact-checked, refined, and aligned with learning objectives.<\/p>\n

My goal was to democratise case creation\u2014enabling any instructor to generate current, customised, and complex cases without sacrificing quality or integrity\u2014and to offer a practical framework that balances AI\u2019s speed with human expertise. Ultimately, I did it to show how thoughtful human\u2013AI collaboration can revolutionize business education, reduce costs, and prepare both faculty and students for an AI-infused future\u2014while still acknowledging the critical role of expert oversight and ongoing research.<\/p>\n

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Does it work?<\/strong><\/p>\n

I find that the LOV AI Co-Creation Approach works remarkably well. By combining the ChatGPT o3 Deep Research model with structured subject-expert verification, I was able to generate full teaching cases and student answer sets\u2014in excess of 20,000 words\u2014in just a few hours, compared to weeks when done manually.<\/p>\n

The Deep Research model\u2019s access to current, reputable sources (e.g., Financial Times, Reuters) meant factual accuracy was extremely high, and I encountered very few errors to correct. Moreover, once I crafted a detailed, 800-word prompt, the output quality was so strong that further iterative prompting was minimal\u2014often it was more efficient to trim content than to ask for revisions. I also transformed extended case outputs into concise, 3\u20134 page vignettes and created derivative interactive resources, demonstrating the approach\u2019s versatility.<\/p>\n

That said, the ownership phase\u2014where I cross-check sources, refine narratives, and adjust pedagogical elements\u2014remains essential. In my experience, LOV AI accelerates case creation without compromising academic rigour, provided that expert oversight stays central.<\/p>\n

The following links give examples of the materials created:<\/p>\n

CoreWeave\u2019s IPO Strategy and Equity Financing \u2013 An Extended Case Study<\/strong><\/p>\n