高等教育中的聊天机器人:探索热情而有能力的虚拟化身对自主学习的影响

IF 8.1 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Shahper Richter, Shohil Kishore, Inna Piven, Patrick Dodd, Guy Bate
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引用次数: 0

摘要

本研究探讨了基于刻板印象内容模型(SCM)的温暖和能力维度设计的拟人化AI聊天机器人如何影响大学生对自主学习(SDL)活动支持的感知。我们研究了学生对两种不同的虚拟形象的反应——一种表现出热情,另一种表现出能力。采用行动设计研究(ADR)方法,我们评估了三门大学课程中的聊天机器人,结合了学生、教育工作者和学习设计师的观点。研究结果显示,学生对虚拟形象的看法不同。温暖的化身提供了更强的情感联系,而有能力的化身提供了更有效的任务导向学习支持。这些结果强调了在聊天机器人设计中平衡热情和能力的重要性,以增强它们对支持SDL参与的感知有用性。该研究还从多个利益相关者的角度对实际实施的挑战和机遇提供了丰富的见解。总之,该研究促进了我们对教育环境中基于scm的聊天机器人设计的理解,并提出了开发学生认为有用的人工智能工具的实用原则,从而为人类与人工智能交互领域做出贡献。目前正在探索人工智能聊天机器人在高等教育中支持自主学习(SDL)方面的潜力。用户对AI系统的感知受到拟人化设计线索的影响,通常通过温暖和能力等维度来理解(与刻板印象内容模型相关)。设计人工智能教育工具需要考虑不同的互动风格(例如,热情vs能力)如何影响学生的参与度和感知有用性。本文增加了学生对基于SCM设计的具有不同程度的温暖和能力的聊天机器人化身的看法的实证见解,以及这些看法如何与他们在大学课程中报告的参与度和对SDL的感知支持相关。有证据表明,学生在聊天机器人化身中区分了热情和能力,将热情与社会情感联系联系起来,将能力与任务相关的学习支持联系起来。一组基于scm的设计原则,用于开发拟人化聊天机器人,旨在被视为对支持SDL有帮助和参与。整合教育者和设计师观点的证据(通过行动设计研究),以揭示超出学生感知的实际实施因素。教育工作者可以根据特定的教学目标和感知到的学生需求(例如,初始参与更热情,复杂任务支持更有能力),选择或倡导适当平衡热情和能力的聊天机器人设计。在为SDL支持实施聊天机器人时,机构应该考虑根据用户感知研究(如SCM)提供的设计,以增加学生接受和感知价值的可能性。关于教育领域人工智能的政策讨论应纳入以用户为中心的设计原则,包括供应链管理维度,以及道德准则,以支持负责任地采用和开发用户认为有效的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning

Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning

Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning

Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning

Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning

This study investigates how anthropomorphic AI chatbot avatars, designed in line with the Stereotype Content Model (SCM) dimensions of warmth and competence, influence university students' perceptions of support for self-directed learning (SDL) activities. We examined student responses to two distinct avatars—one projecting warmth and the other projecting competence. Using an Action Design Research (ADR) approach, we evaluated the chatbots across three university courses, incorporating perspectives from students, educators and learning designers. Findings reveal that students perceive the avatars differently. The warm avatar provides a stronger emotional connection, while the competent avatar offers more effective task-oriented learning support. These results highlight the importance of balancing warmth and competence in chatbot design to enhance their perceived usefulness for supporting SDL engagement. The study also supplies rich insights into practical implementation challenges and opportunities from multiple stakeholder viewpoints. Altogether, the research advances our understanding of SCM-informed chatbot design in educational settings and proposes practical principles for developing AI tools that students perceive as helpful, thereby contributing to the field of human–AI interaction.

Practitioner notes

What is already known about this topic

  • The potential of AI chatbots to support aspects of self-directed learning (SDL) in higher education is currently being explored.
  • User perceptions of AI systems are influenced by anthropomorphic design cues, often understood through dimensions like warmth and competence (related to the Stereotype Content Model—SCM).
  • Designing AI educational tools requires considering how different interactional styles (eg, warmth vs. competence) can affect student engagement and perceived usefulness.

What this paper adds

  • Empirical insights into students' perceptions of chatbot avatars designed with varying levels of warmth and competence, based on the SCM, and how these perceptions relate to their reported engagement and perceived support for SDL in university courses.
  • Evidence that students distinguish between warmth and competence in chatbot avatars, associating warmth with socio-emotional connection and competence with task-related learning support.
  • A set of SCM-informed design principles for developing anthropomorphic chatbots intended to be perceived as helpful and engaging for supporting SDL.
  • Evidence of integrating educator and designer perspectives (through Action Design Research) to uncover practical implementation factors beyond student perceptions alone.

Implications for practice and/or policy

  • Educators can select or advocate for chatbot designs that appropriately balance warmth and competence based on specific pedagogical goals and perceived student needs (eg, more warmth for initial engagement and more competence for complex task support).
  • When implementing chatbots for SDL support, institutions should consider designs informed by user perception research (like SCM) to increase the likelihood of student acceptance and perceived value.
  • Policy discussions on AI in education should incorporate user-centred design principles, including SCM dimensions, alongside ethical guidelines to support responsible adoption and the development of tools perceived as effective by users.
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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