Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
- URL: http://arxiv.org/abs/2404.11072v1
- Date: Wed, 17 Apr 2024 05:05:05 GMT
- Title: Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
- Authors: Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi, Aneesha Bakharia, Shazia Sadiq, Dragan Gasevic, Paul Denny, Hassan Khosravi,
- Abstract summary: We present a framework for incorporating GenAI into educational tools and demonstrate its application in our tool, Feedback Copilot.
This work charts a course for the future of GenAI in education.
- Score: 6.626949691937476
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversational user interfaces (CUIs) for tasks like generating materials or providing feedback. However, this presents challenges including the need for educator expertise in AI and CUIs, ethical concerns with high-stakes decisions, and privacy risks. CUIs also struggle with complex tasks. To address these, we propose transitioning from CUIs to user-friendly applications leveraging LLMs via API calls. We present a framework for ethically incorporating GenAI into educational tools and demonstrate its application in our tool, Feedback Copilot, which provides personalized feedback on student assignments. Our evaluation shows the effectiveness of this approach, with implications for GenAI researchers, educators, and technologists. This work charts a course for the future of GenAI in education.
Related papers
- KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [75.78948575957081]
Large language models (LLMs) usually rely on retrieval-augmented generation to exploit knowledge materials in an instant manner.
We propose KBAlign, an approach designed for efficient adaptation to downstream tasks involving knowledge bases.
Our method utilizes iterative training with self-annotated data such as Q&A pairs and revision suggestions, enabling the model to grasp the knowledge content efficiently.
arXiv Detail & Related papers (2024-11-22T08:21:03Z) - LLMs Integration in Software Engineering Team Projects: Roles, Impact, and a Pedagogical Design Space for AI Tools in Computing Education [7.058964784190549]
This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot.
Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics.
arXiv Detail & Related papers (2024-10-30T14:43:33Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - Human-Centric eXplainable AI in Education [0.0]
This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape.
It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools.
It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
arXiv Detail & Related papers (2024-10-18T14:02:47Z) - BoilerTAI: A Platform for Enhancing Instruction Using Generative AI in Educational Forums [0.0]
This paper describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums.
The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM)
arXiv Detail & Related papers (2024-09-20T04:00:30Z) - Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces.
This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
arXiv Detail & Related papers (2024-08-27T13:50:37Z) - ChatISA: A Prompt-Engineered Chatbot for Coding, Project Management, Interview and Exam Preparation Activities [0.6784745592354215]
ChatISA is a robust tool that addresses coding inquiries, project management, exam preparation, and interview readiness.
The implementation of ChatISA revealed significant insights and challenges, including the necessity of ethical guidelines and balancing AI usage with maintaining student agency.
All code for ChatISA is made publicly available on GitHub, enabling other institutions to customize and integrate similar AI-driven educational tools within their curricula.
arXiv Detail & Related papers (2024-06-13T04:00:07Z) - Innovating Computer Programming Pedagogy: The AI-Lab Framework for
Generative AI Adoption [0.0]
We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses.
By identifying and rectifying GenAI's errors, students enrich their learning process.
For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
arXiv Detail & Related papers (2023-08-23T17:20:37Z) - GenNI: Human-AI Collaboration for Data-Backed Text Generation [102.08127062293111]
Table2Text systems generate textual output based on structured data utilizing machine learning.
GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text.
arXiv Detail & Related papers (2021-10-19T18:07:07Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Explainable Active Learning (XAL): An Empirical Study of How Local
Explanations Impact Annotator Experience [76.9910678786031]
We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting.
Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
arXiv Detail & Related papers (2020-01-24T22:52:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.