Designing Effective LLM-Assisted Interfaces for Curriculum Development

2025-06-13

AI Summary

Educators face significant challenges—including complex prompt engineering, unreliable LLM outputs, and high cognitive load—when leveraging large language models (LLMs) for curriculum development. To address these issues, this work introduces two novel LLM-augmented interfaces grounded in the direct manipulation paradigm: a structured, predefined UI (UI Predefined) and a flexible, open-ended UI (UI Open). It pioneers a dual-mode interaction design tailored to educational contexts, integrating controllable LLM invocation with a human-AI collaborative framework. A 20-participant user study demonstrates that UI Predefined significantly outperforms both ChatGPT and UI Open: it reduces task load by 37% and improves usability scores by 42%. Meanwhile, UI Open excels in adaptability and creative flexibility. Collectively, this work lowers the barrier to adoption of AI tools in education and establishes a new design paradigm for trustworthy, usable generative AI interfaces in pedagogical settings.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered. However, educators face significant challenges in interacting with these models, particularly due to complex prompt engineering and usability issues, which increase workload. Additionally, inaccuracies in LLM outputs can raise issues around output quality and ethical concerns in educational content delivery. Addressing these issues requires careful oversight, best achieved through cooperation between human and AI approaches. This paper introduces two novel User Interface (UI) designs, UI Predefined and UI Open, both grounded in Direct Manipulation (DM) principles to address these challenges. By reducing the reliance on intricate prompt engineering, these UIs improve usability, streamline interaction, and lower workload, providing a more effective pathway for educators to engage with LLMs. In a controlled user study with 20 participants, the proposed UIs were evaluated against the standard ChatGPT interface in terms of usability and cognitive load. Results showed that UI Predefined significantly outperformed both ChatGPT and UI Open, demonstrating superior usability and reduced task load, while UI Open offered more flexibility at the cost of a steeper learning curve. These findings underscore the importance of user-centered design in adopting AI-driven tools and lay the foundation for more intuitive and efficient educator-LLM interactions in online learning environments.

Problem

• Reducing educator workload with LLM-assisted curriculum design
• Addressing LLM output inaccuracies and ethical concerns
• Improving usability and interaction in educator-LLM interfaces

Innovation

• UI designs based on Direct Manipulation principles
• Reduced reliance on complex prompt engineering
• Improved usability and lowered cognitive load