# Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructors

arXiv: https://arxiv.org/abs/2503.05039 · HTML: https://mlsanity.com/paper/2503.05039/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria](https://mlsanity.com/paper/2309.13633/) (2023)
- [Generative Students: Using LLM-Simulated Student Profiles to Support Question Item Evaluation](https://mlsanity.com/paper/2405.11591/) (2024)
- [Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling](https://mlsanity.com/paper/2402.01927/) (2024)
- [Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms](https://mlsanity.com/paper/2009.11100/) (2020)
- [LLM-Driven Learning Analytics Dashboard for Teachers in EFL Writing Education](https://mlsanity.com/paper/2410.15025/) (2024)
- [Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement](https://mlsanity.com/paper/2304.00903/) (2023)
- [Who is Helping Whom? Student Concerns about AI- Teacher Collaboration in Higher Education Classrooms](https://mlsanity.com/paper/2412.14469/) (2024)

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

- [Building Scaffolding Dialogue Data with LLM-Simulated Novices](https://mlsanity.com/paper/2508.04428/) (2025)
- [Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice](https://mlsanity.com/paper/2509.12107/) (2025)
- [TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors](https://mlsanity.com/paper/2603.18189/) (2026)
- [Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence](https://mlsanity.com/paper/2605.14266/) (2026)

## Abstract (arXiv)

Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design AI tools that support broader instructor adoption. This study applies a human-centered design approach, incorporating qualitative methods, to investigate the design of interactive pedagogical agents that provide instructional suggestions in response to instructors' questions. We conducted a formative study involving interviews with five pedagogy experts to examine existing strategies for supporting instructors' pedagogical needs. Building on these insights, we facilitated a participatory design session with ten pedagogy experts, where participants reviewed a storyboard depicting a chatbot designed for instructors with varying levels of AI literacy and differing attitudes toward AI. Experts also evaluated the quality of LLM-generated suggestions based on common teaching challenges. Our findings highlight the need for chatbot interactions that foster trust, especially for AI-conservative instructors. Experts emphasized the importance of social transparency (for example, showing how peers use the tool) and allowing instructors to flexibly control how much or how little they engage with the system. We also propose design recommendations to enhance the quality of AI-generated teaching suggestions, such as adapting them to reflect instructors' prior teaching experience. This work underscores the urgent need to support AI-conservative instructors, as AI literacy and attitudes are closely intertwined. Without thoughtful design, there is a risk of widening pedagogical divides and reducing students' learning opportunities.