AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories
- URL: http://arxiv.org/abs/2508.00970v1
- Date: Fri, 01 Aug 2025 15:44:19 GMT
- Title: AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories
- Authors: Ning Yu, Jie Zhang, Sandeep Mitra, Rebecca Smith, Adam Rich,
- Abstract summary: This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI)<n>The framework emphasizes transparency, self-regulated learning, and pedagogical oversight.
- Score: 8.500617875591633
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI) to support reflective, iterative learning. Implemented in EduAlly, an AI-assisted platform for writing-intensive and feedback-sensitive tasks, the framework emphasizes transparency, self-regulated learning, and pedagogical oversight. A mixed-methods study was piloted at a comprehensive public university to evaluate alignment between AI-generated feedback, instructor evaluations, and student self-assessments; the impact of iterative revision on performance; and student perceptions of AI feedback. Quantitative results demonstrated statistically significant improvement between first and second attempts, with agreement between student self-evaluations and final instructor grades. Qualitative findings indicated students valued immediacy, specificity, and opportunities for growth that AI feedback provided. These findings validate the potential to enhance student learning outcomes through developmentally grounded, ethically aligned, and scalable AI feedback systems. The study concludes with implications for future interdisciplinary applications and refinement of AI-supported educational technologies.
Related papers
- AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind [2.0272430076690027]
This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind.<n>Data from 309 undergraduates in Computer Science and Business courses were collected.
arXiv Detail & Related papers (2025-07-29T10:11:24Z) - Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications [0.2812395851874055]
This study evaluates the use of the Educational AI Hub, an AI-powered learning framework, in undergraduate civil and environmental engineering courses at a large R1 public university.<n>Students appreciated the AI assistant for its convenience and comfort, with nearly half reporting greater ease in using the AI tool.<n>While most students viewed AI use as ethically acceptable, many expressed uncertainties about institutional policies and apprehension about potential academic misconduct.
arXiv Detail & Related papers (2025-06-06T03:02:49Z) - When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
arXiv Detail & Related papers (2025-06-05T20:48:16Z) - Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
arXiv Detail & Related papers (2025-04-01T13:53:11Z) - Synergizing Self-Regulation and Artificial-Intelligence Literacy Towards Future Human-AI Integrative Learning [92.34299949916134]
Self-regulated learning (SRL) and Artificial-Intelligence (AI) literacy are becoming key competencies for successful human-AI interactive learning.<n>This study analyzed data from 1,704 Chinese undergraduates using clustering methods to uncover four learner groups.
arXiv Detail & Related papers (2025-03-31T13:41:21Z) - Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance [0.0]
The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis.<n>Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA.<n>Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified.
arXiv Detail & Related papers (2024-12-03T04:51:57Z) - Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration [0.0]
This paper presents the Comprehensive AI Assessment Framework (CAIAF), an evolved version of the AI Assessment Scale (AIAS) by Perkins, Furze, Roe, and MacVaugh.
The CAIAF incorporates stringent ethical guidelines, with clear distinctions based on educational levels, and advanced AI capabilities.
The framework will ensure better learning outcomes, uphold academic integrity, and promote responsible use of AI.
arXiv Detail & Related papers (2024-06-07T07:18:42Z) - From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education [0.0]
Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension.
This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators.
It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications.
arXiv Detail & Related papers (2024-02-05T16:12:14Z) - 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) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - A general framework for scientifically inspired explanations in AI [76.48625630211943]
We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented.
This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
arXiv Detail & Related papers (2020-03-02T10:32:21Z)
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.