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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.11767 |
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| _version_ | 1866909648498458624 |
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| author | Faraji, Abdolali Tavakoli, Mohammadreza Moein, Mohammad Molavi, Mohammadreza Kismihók, Gábor |
| author_facet | Faraji, Abdolali Tavakoli, Mohammadreza Moein, Mohammad Molavi, Mohammadreza Kismihók, Gábor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing Effective LLM-Assisted Interfaces for Curriculum Development Faraji, Abdolali Tavakoli, Mohammadreza Moein, Mohammad Molavi, Mohammadreza Kismihók, Gábor Computers and Society 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. |
| title | Designing Effective LLM-Assisted Interfaces for Curriculum Development |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2506.11767 |