# PAPPL: Personalized AI-Powered Progressive Learning Platform

arXiv: https://arxiv.org/abs/2508.14109 · HTML: https://mlsanity.com/paper/2508.14109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 18 Aug 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Tool Learning with Foundation Models](https://mlsanity.com/paper/2304.08354/) (2023)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System](https://mlsanity.com/paper/2404.17460/) (2024)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring](https://mlsanity.com/paper/2401.08517/) (2024)
- [Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support](https://mlsanity.com/paper/2412.11995/) (2024)
- [A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM](https://mlsanity.com/paper/2005.06616/) (2020)
- [Predicting Learning Performance with Large Language Models: A Study in Adult Literacy](https://mlsanity.com/paper/2403.14668/) (2024)
- [An LLM-Guided Tutoring System for Social Skills Training](https://mlsanity.com/paper/2501.09870/) (2025)
- [The promise and limits of LLMs in constructing proofs and hints for logic problems in intelligent tutoring systems](https://mlsanity.com/paper/2505.04736/) (2025)

## Abstract (arXiv)

Engineering education has historically been constrained by rigid, standardized frameworks, often neglecting students' diverse learning needs and interests. While significant advancements have been made in online and personalized education within K-12 and foundational sciences, engineering education at both undergraduate and graduate levels continues to lag in adopting similar innovations. Traditional evaluation methods, such as exams and homework assignments, frequently overlook individual student requirements, impeding personalized educational experiences. To address these limitations, this paper introduces the Personalized AI-Powered Progressive Learning (PAPPL) platform, an advanced Intelligent Tutoring System (ITS) designed specifically for engineering education. It highlights the development of a scalable, data-driven tutoring environment leveraging cutting-edge AI technology to enhance personalized learning across diverse academic disciplines, particularly in STEM fields. PAPPL integrates core ITS components including the expert module, student module, tutor module, and user interface, and utilizes GPT-4o, a sophisticated large language model (LLM), to deliver context-sensitive and pedagogically sound hints based on students' interactions. The system uniquely records student attempts, detects recurring misconceptions, and generates progressively targeted feedback, providing personalized assistance that adapts dynamically to each student's learning profile. Additionally, PAPPL offers instructors detailed analytics, empowering evidence-based adjustments to teaching strategies. This study provides a fundamental framework for the progression of Generative ITSs scalable to all education levels, delivering important perspectives on personalized progressive learning and the wider possibilities of Generative AI in the field of education.