Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning

• URL: http://arxiv.org/abs/2410.08922v1
• Date: Fri, 11 Oct 2024 15:49:42 GMT
• Title: Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning
• Authors: Majeed Kazemitabaar, Oliver Huang, Sangho Suh, Austin Z. Henley, Tovi Grossman,
• Abstract summary: We develop seven cognitive engagement techniques aimed at promoting deeper engagement with AI-generated code. Our results highlight the most effective technique: guiding learners through the step-by-step problem-solving process.
• Score: 14.051451035773045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill development. To address this issue, we conducted a systematic design exploration to develop seven cognitive engagement techniques aimed at promoting deeper engagement with AI-generated code. In this paper, we describe our design process, the initial seven techniques and results from a between-subjects study (N=82). We then iteratively refined the top techniques and further evaluated them through a within-subjects study (N=42). We evaluate the friction each technique introduces, their effectiveness in helping learners apply concepts to isomorphic tasks without AI assistance, and their success in aligning learners' perceived and actual coding abilities. Ultimately, our results highlight the most effective technique: guiding learners through the step-by-step problem-solving process, where they engage in an interactive dialog with the AI, prompting what needs to be done at each stage before the corresponding code is revealed.

Related papers

• BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
  We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs) The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM. In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-10-05T09:27:52Z)
• The why, what, and how of AI-based coding in scientific research [0.0]
  Generative AI, particularly large language models (LLMs), has the potential to transform coding into intuitive conversations. We dissect AI-based coding through three key lenses. We address the limitations and future outlook of AI in coding.
  arXiv  Detail & Related papers  (2024-10-03T02:36:30Z)
• Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning [3.187381965457262]
  This paper proposes a novel approach that merges Cognitive AI and Generative AI to address these challenges. We employ a structured knowledge representation, the TMK (Task-Method-Knowledge) model, to encode skills taught in an online Knowledge-based AI course.
  arXiv  Detail & Related papers  (2024-07-28T04:21:22Z)
• Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study [6.028558240668647]
  We partner with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged hands-on' approach led to a co-development of students' practical and critical competencies.
  arXiv  Detail & Related papers  (2024-05-07T21:34:10Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Toward enriched Cognitive Learning with XAI [44.99833362998488]
  We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
  arXiv  Detail & Related papers  (2023-12-19T16:13:47Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• ActiveAI: Introducing AI Literacy for Middle School Learners with Goal-based Scenario Learning [0.0]
  The ActiveAI project addresses key challenges in AI education for grades 7-9 students. The app incorporates a variety of learner inputs like sliders, steppers, and collectors to enhance understanding. The project is currently in the implementation stage, leveraging the intelligent tutor design principles for app development.
  arXiv  Detail & Related papers  (2023-08-21T11:43:43Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• A Competence-aware Curriculum for Visual Concepts Learning via Question Answering [95.35905804211698]
  We propose a competence-aware curriculum for visual concept learning in a question-answering manner. We design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-07-03T05:08:09Z)

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.