Bidirectional Human-AI Alignment in Education for Trustworthy Learning Environments
- URL: http://arxiv.org/abs/2512.21552v1
- Date: Thu, 25 Dec 2025 07:50:56 GMT
- Title: Bidirectional Human-AI Alignment in Education for Trustworthy Learning Environments
- Authors: Hua Shen,
- Abstract summary: Artificial intelligence (AI) is transforming education, offering unprecedented opportunities to personalize learning, enhance assessment, and support educators.<n>Yet these opportunities also introduce risks related to equity, privacy, and student autonomy.<n>This chapter develops the concept of bidirectional human-AI alignment in education, emphasizing that trustworthy learning environments arise not only from embedding human values into AI systems but also from equipping teachers, students, and institutions with the skills to interpret, critique, and guide these technologies.
- Score: 7.0064528229443
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial intelligence (AI) is transforming education, offering unprecedented opportunities to personalize learning, enhance assessment, and support educators. Yet these opportunities also introduce risks related to equity, privacy, and student autonomy. This chapter develops the concept of bidirectional human-AI alignment in education, emphasizing that trustworthy learning environments arise not only from embedding human values into AI systems but also from equipping teachers, students, and institutions with the skills to interpret, critique, and guide these technologies. Drawing on emerging research and practical case examples, we explore AI's evolution from support tool to collaborative partner, highlighting its impacts on teacher roles, student agency, and institutional governance. We propose actionable strategies for policymakers, developers, and educators to ensure that AI advances equity, transparency, and human flourishing rather than eroding them. By reframing AI adoption as an ongoing process of mutual adaptation, the chapter envisions a future in which humans and intelligent systems learn, innovate, and grow together.
Related papers
- Charting the Future of AI-supported Science Education: A Human-Centered Vision [0.9851520275517003]
The chapter synthesizes developments across five dimensions: educational goals, instructional procedures, learning materials, assessment, and outcomes.<n>We argue that AI offers transformative potential to enrich inquiry, personalize learning, and support teacher practice, but only when guided by Responsible and Ethical Principles (REP)<n>The REP framework, emphasizing fairness, transparency, privacy, accountability, and respect for human values, anchors our vision for AI-supported science education.
arXiv Detail & Related papers (2026-02-09T00:06:00Z) - Integrating Generative AI into LMS: Reshaping Learning and Instructional Design [1.2489632787815885]
We propose two guiding principles for integrating generative AI into Learning Management Systems.<n>First, From Content Delivery to Fostering Higher-Order Thinking, emphasizing AI's role in supporting inquiry, collaboration, and reflective knowledge building.<n>Second, Toward Meaningful Interaction with AI, highlighting the design of learning environments that nurture critical, intentional, and socially mediated engagement with AI.
arXiv Detail & Related papers (2025-10-20T18:58:47Z) - A principled way to think about AI in education: guidance for action based on goals, models of human learning, and use of technologies [0.0]
I articulate a set of principles that connect broad our educational goalsto actionable practices.<n>The piece illustrates how a principled approach enables higher education to harness new tools while preserving its fundamental mission.
arXiv Detail & Related papers (2025-10-01T21:19:12Z) - Perspectives and potential issues in using artificial intelligence for computer science education [0.0]
ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions.<n>While AI technologies hold potential for enhancing learning experiences, there are also emerging concerns.<n>These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations.
arXiv Detail & Related papers (2025-09-17T06:34:23Z) - 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) - Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
arXiv Detail & Related papers (2024-11-29T14:55:15Z) - Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation [0.0]
Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement.
This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps.
arXiv Detail & Related papers (2024-11-24T19:53:48Z) - Collaborative Design of AI-Enhanced Learning Activities [0.0]
We develop a formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices.
Participants reflect on AI's potential in teaching and learning by exploring different activities that can integrate AI literacy in education, including its ethical considerations and potential for innovative pedagogy.
arXiv Detail & Related papers (2024-07-09T08:34:08Z) - 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) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - 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) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
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.