Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants

• URL: http://arxiv.org/abs/2309.10419v1
• Date: Tue, 19 Sep 2023 08:30:58 GMT
• Title: Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants
• Authors: Changyoon Lee, Junho Myung, Jieun Han, Jiho Jin and Alice Oh
• Abstract summary: We investigate the practicality of using generative AI as TAs in programming education by examining novice learners' interaction with TAs in a subgoal learning environment. Our study shows that learners can solve tasks faster with comparable scores with AI TAs. We suggest guidelines to better design and utilize generative AI as TAs in programming education from the result of our chat log analysis.
• Score: 18.14390906820148
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With recent advances in generative AI, conversational models like ChatGPT have become feasible candidates for TAs. We investigate the practicality of using generative AI as TAs in introductory programming education by examining novice learners' interaction with TAs in a subgoal learning environment. To compare the learners' interaction and perception of the AI and human TAs, we conducted a between-subject study with 20 novice programming learners. Learners solve programming tasks by producing subgoals and subsolutions with the guidance of a TA. Our study shows that learners can solve tasks faster with comparable scores with AI TAs. Learners' perception of the AI TA is on par with that of human TAs in terms of speed and comprehensiveness of the replies and helpfulness, difficulty, and satisfaction of the conversation. Finally, we suggest guidelines to better design and utilize generative AI as TAs in programming education from the result of our chat log analysis.

Related papers

• AI-Tutoring in Software Engineering Education [0.7631288333466648]
  We conducted an exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis. The findings highlight advantages, such as timely feedback and scalability. However, challenges like generic responses and students' concerns about a learning progress inhibition when using the AI-Tutor were also evident.
  arXiv  Detail & Related papers  (2024-04-03T08:15:08Z)
• I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant [0.0]
  This study examined the perceptions of ten online and distance learning students regarding the design of a hypothetical AI Digital Assistant (AIDA) All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education.
  arXiv  Detail & Related papers  (2024-02-16T08:10:41Z)
• AI for non-programmers: Applied AI in the lectures for students without programming skills [0.0]
  This work presents a didactic planning script for applied AI. The didactic planning script is based on the AI application pipeline and links AI concepts with study-relevant topics. An example lecture series for master students in energy management shows how AI can be seamlessly integrated into discipline-specific lectures.
  arXiv  Detail & Related papers  (2024-02-06T17:26:24Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students [35.05435052195561]
  The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes. This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for assessment.
  arXiv  Detail & Related papers  (2024-01-26T14:49:29Z)
• AI-assisted Learning for Electronic Engineering Courses in High Education [2.67766280323297]
  The study includes the evaluation and reflection of different stakeholders: students, lecturers, and engineers. The findings of this study shed light on the benefits and limitations of ChatGPT as an AI tool, paving the way for innovative learning approaches in technical disciplines.
  arXiv  Detail & Related papers  (2023-11-02T07:48:10Z)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates [53.04281982845422]
  We present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE) Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes.
  arXiv  Detail & Related papers  (2022-02-02T20:59:53Z)
• The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
  The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
  arXiv  Detail & Related papers  (2021-07-05T12:18:17Z)
• 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.