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
- Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces [0.31873871499564926]
We introduce generative AI capabilities to assist educators in creating tutor interfaces.
Our approach leverages Large Language Models (LLMs) and prompt engineering to generate tutor layout and contents.
A small-scale comparison shows the potential of our approach to enhance the efficiency of tutor interface design.
arXiv Detail & Related papers (2024-05-23T15:46:10Z) - How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey [48.97104365617498]
The emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI.
This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development.
arXiv Detail & Related papers (2024-03-21T15:44:56Z) - How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation [0.0]
The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history.
The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations.
arXiv Detail & Related papers (2023-11-29T15:02:46Z) - A Framework for Responsible Development of Automated Student Feedback
with Generative AI [3.0456580409182155]
Recent advances in generative AI provide the opportunity to deliver repeatable, scalable and instant automatically generated feedback to students.
This article will outline the frontiers of automated feedback, identify the ethical issues involved in the provision of automated feedback and present a framework to assist academics to develop such systems responsibly.
arXiv Detail & Related papers (2023-08-29T14:29:57Z) - 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) - Towards Applying Powerful Large AI Models in Classroom Teaching:
Opportunities, Challenges and Prospects [5.457842083043013]
This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching.
We explore the potential of AI to augment and enrich teacher-student dialogues and improve the quality of teaching.
arXiv Detail & Related papers (2023-05-05T11:09:13Z) - OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
Human Intelligence (HI) excels at combining basic skills to solve complex tasks.
This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents.
We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
arXiv Detail & Related papers (2023-04-10T03:55:35Z) - Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions.
By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
arXiv Detail & Related papers (2022-11-27T13:07:14Z) - 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.