# Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives arXiv: https://arxiv.org/abs/2306.17459 ยท HTML: https://mlsanity.com/paper/2306.17459/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 29 references that are in the map. - First posted on arXiv 30 Jun 2023. ## Builds on (most cited first, 15 of 15) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023) - [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022) - [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022) - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022) - [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023) - [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023) - [On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?](https://mlsanity.com/paper/2303.11146/) (2023) - [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023) - [Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models](https://mlsanity.com/paper/2302.04662/) (2023) - [Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code](https://mlsanity.com/paper/2303.08033/) (2023) - [Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?](https://mlsanity.com/paper/2306.13906/) (2023) - [Many bioinformatics programming tasks can be automated with ChatGPT](https://mlsanity.com/paper/2303.13528/) (2023) - [Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects](https://mlsanity.com/paper/2305.03433/) (2023) - [Document Chunking and Learning Objective Generation for Instruction Design](https://mlsanity.com/paper/1806.01351/) (2018) ## Built on by (most cited first, 16 of 16) - [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023) - [Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation](https://mlsanity.com/paper/2408.04394/) (2024) - [CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer](https://mlsanity.com/paper/2406.10296/) (2024) - [VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation](https://mlsanity.com/paper/2403.12415/) (2024) - [LLM Attributor: Interactive Visual Attribution for LLM Generation](https://mlsanity.com/paper/2404.01361/) (2024) - [Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents](https://mlsanity.com/paper/2504.07655/) (2025) - [Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning](https://mlsanity.com/paper/2404.18262/) (2024) - ["Give me the code" -- Log Analysis of First-Year CS Students' Interactions With GPT](https://mlsanity.com/paper/2411.17855/) (2024) - [Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models](https://mlsanity.com/paper/2401.10759/) (2024) - [CurricuLLM: Designing Personalized and Workforce-Aligned Cybersecurity Curricula Using Fine-Tuned LLMs](https://mlsanity.com/paper/2601.04940/) (2026) - [Automatic Classification of Pedagogical Materials against CS Curriculum Guidelines](https://mlsanity.com/paper/2602.03962/) (2026) - [The Advancement of Personalized Learning Potentially Accelerated by Generative AI](https://mlsanity.com/paper/2412.00691/) (2024) - [Bridging LMS and generative AI: dynamic course content integration (DCCI) for enhancing student satisfaction and engagement via the ask ME assistant](https://mlsanity.com/paper/2504.03966/) (2025) - [Harnessing IoT and Generative AI for Weather-Adaptive Learning in Climate Resilience Education](https://mlsanity.com/paper/2508.21666/) (2025) - [LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure](https://mlsanity.com/paper/2510.18877/) (2025) - [Stan: An LLM-based thermodynamics course assistant](https://mlsanity.com/paper/2603.04657/) (2026) ## Abstract (arXiv) We evaluated the capability of a generative pre-trained transformer (GPT-4) to automatically generate high-quality learning objectives (LOs) in the context of a practically oriented university course on Artificial Intelligence. Discussions of opportunities (e.g., content generation, explanation) and risks (e.g., cheating) of this emerging technology in education have intensified, but to date there has not been a study of the models' capabilities in supporting the course design and authoring of LOs. LOs articulate the knowledge and skills learners are intended to acquire by engaging with a course. To be effective, LOs must focus on what students are intended to achieve, focus on specific cognitive processes, and be measurable. Thus, authoring high-quality LOs is a challenging and time consuming (i.e., expensive) effort. We evaluated 127 LOs that were automatically generated based on a carefully crafted prompt (detailed guidelines on high-quality LOs authoring) submitted to GPT-4 for conceptual modules and projects of an AI Practitioner course. We analyzed the generated LOs if they follow certain best practices such as beginning with action verbs from Bloom's taxonomy in regards to the level of sophistication intended. Our analysis showed that the generated LOs are sensible, properly expressed (e.g., starting with an action verb), and that they largely operate at the appropriate level of Bloom's taxonomy, respecting the different nature of the conceptual modules (lower levels) and projects (higher levels). Our results can be leveraged by instructors and curricular designers wishing to take advantage of the state-of-the-art generative models to support their curricular and course design efforts.