# Large Language Models Meet User Interfaces: The Case of Provisioning Feedback

arXiv: https://arxiv.org/abs/2404.11072 · HTML: https://mlsanity.com/paper/2404.11072/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 88 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 30 of 37)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Cognitive Mirage: A Review of Hallucinations in Large Language Models](https://mlsanity.com/paper/2309.06794/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria](https://mlsanity.com/paper/2309.13633/) (2023)
- [LLM-based NLG Evaluation: Current Status and Challenges](https://mlsanity.com/paper/2402.01383/) (2024)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models](https://mlsanity.com/paper/2305.11483/) (2023)
- [How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models](https://mlsanity.com/paper/2209.01390/) (2022)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [Opal: Multimodal Image Generation for News Illustration](https://mlsanity.com/paper/2204.09007/) (2022)
- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (2023)
- [Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts](https://mlsanity.com/paper/2308.03921/) (2023)
- [Supporting Sensemaking of Large Language Model Outputs at Scale](https://mlsanity.com/paper/2401.13726/) (2024)
- [Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level](https://mlsanity.com/paper/2311.07434/) (2023)
- [Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods](https://mlsanity.com/paper/2307.08161/) (2023)
- [Prompting for Discovery: Flexible Sense-Making for AI Art-Making with Dreamsheets](https://mlsanity.com/paper/2310.09985/) (2023)
- [Interactive AI Alignment: Specification, Process, and Evaluation Alignment](https://mlsanity.com/paper/2311.00710/) (2023)
- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game](https://mlsanity.com/paper/2306.16639/) (2023)
- [GenAssist: Making Image Generation Accessible](https://mlsanity.com/paper/2307.07589/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Survey on Collaborative Mechanisms Between Large and Small Language Models](https://mlsanity.com/paper/2505.07460/) (2025)
- [Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation](https://mlsanity.com/paper/2501.01793/) (2025)
- [AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agents](https://mlsanity.com/paper/2604.20279/) (2026)
- [Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation](https://mlsanity.com/paper/2503.15762/) (2025)

## Abstract (arXiv)

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.