Learning and Motivation最新文献_第5页

Feedback valence and framing in AI-mediated EFL learning: A quantum-inspired analysis of their effects on goal orientation, motivational affect, and task persistence through achievement goal theory 人工智能介导的英语学习中的反馈效价和框架：基于成就目标理论对其对目标取向、动机影响和任务持久性影响的量子启发分析

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-09 DOI: 10.1016/j.lmot.2025.102200

Ehsan Namaziandost , Ferdi Çelik , Volkan Duran

As artificial intelligence (AI) increasingly shapes language learning, the role of feedback in AI-mediated environments has become a focal concern—particularly in how it influences learner motivation, affect, and engagement. Grounded in Achievement Goal Theory and enriched by a quantum-inspired analysis, this mixed-methods study examined how feedback valence (positive vs. negative) and framing (process-oriented vs. outcome-oriented) jointly impact English as a Foreign Language (EFL) learners’ goal orientation, motivational affect, and task persistence. The study also explored non-classical cognitive patterns, including emotional ambivalence, decision reversals, and motivational interference. A total of 120 undergraduate EFL students were randomly assigned to one of four feedback conditions during an eight-session ChatGPT-based grammar course. Quantitative data were gathered through validated instruments measuring goal orientation, motivational affect, and task persistence. Qualitative data from reflection logs and semi-structured interviews were analyzed thematically to uncover deeper cognitive-emotional dynamics. Results from MANOVA and follow-up ANOVAs revealed that positive, process-oriented feedback significantly enhanced mastery goals, positive affect, and persistence, whereas negative, outcome-oriented feedback resulted in declines across these domains. Qualitative findings uncovered complex, nonlinear responses including dual emotional states, motivational conflicts, and cognitive interference, which are thepatterns consistent with quantum-inspired models of cognition. This study offers both theoretical and practical implications, highlighting the importance of feedback design in AI-supported instruction. It underscores how subtle variations in feedback framing and tone can generate divergent motivational trajectories, and introduces a novel quantum-inspired lens to capture the probabilistic, emotionally dynamic nature of learner cognition in digitally mediated settings.

随着人工智能（AI）越来越多地影响语言学习，反馈在人工智能介导的环境中的作用已经成为人们关注的焦点，特别是它如何影响学习者的动机、影响和参与。本研究以成就目标理论为基础，以量子启发的分析为基础，研究了反馈效价（积极与消极）和框架（过程导向与结果导向）如何共同影响作为外语的英语学习者的目标取向、动机影响和任务持久性。研究还探讨了非经典认知模式，包括情绪矛盾、决策逆转和动机干扰。总共有120名本科生被随机分配到四种反馈条件中的一种，在八节基于chatgpt的语法课程中。定量数据收集通过验证的工具测量目标取向，动机影响和任务持久性。从反思日志和半结构化访谈中获得的定性数据进行了主题分析，以揭示更深层次的认知-情感动态。方差分析和后续方差分析结果显示，以过程为导向的积极反馈显著提高了掌握目标、积极影响和持久性，而以结果为导向的消极反馈则导致这些领域的下降。定性研究结果揭示了复杂的非线性反应，包括双重情绪状态、动机冲突和认知干扰，这些都是与量子启发的认知模型一致的模式。本研究提供了理论和实践意义，突出了反馈设计在人工智能支持教学中的重要性。它强调了反馈框架和语气的微妙变化如何产生不同的动机轨迹，并引入了一种新颖的量子启发镜头，以捕捉数字媒介环境中学习者认知的概率性和情感动态性。

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引用次数: 0

An exploratory analysis of reinforcer competition and novelty in domestic dogs 家犬强化物竞争与新颖性的探索性分析

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-09 DOI: 10.1016/j.lmot.2025.102204

Valdeep Saini , Asude Ayvaci , Tina Vo

Reinforcer competition in domestic dogs by studied by exploring their preferences between social (human-mediated) and non-social (environmental) reinforcers under varying conditions of familiarity and novelty. Five pet dogs were presented with choices involving either their owner or a stranger in either familiar or unfamiliar contexts. Each scenario was further split into passive and active conditions, depending on whether the human attempted to solicit attention. Dogs’ sociability was measured through contact latency, proximity, and gaze rate. Results revealed that dogs consistently preferred social reinforcement from owners, regardless of environmental novelty. They were quicker to approach and spent more time with owners, and gaze rate was generally higher toward them. In contrast, dogs demonstrated less sociability toward strangers, especially in novel contexts, indicating that social reinforcer novelty did not increase preference. However, non-social novelty did appear to influence behavior, as dogs showed increased attention toward novel environments at the expense of social interaction, especially in the presence of strangers. Active behavior by the human (e.g., calling the dog’s name) increased sociability measures across all conditions, though most changes were not statistically significant. Findings suggest that the reinforcing value of owner social interaction generally outweighs that of non-social novelty, whereas a stranger’s social interaction is less preferred than even non-social stimulation. These results have implications for understanding dog behavior in human-centered environments and highlight the influence of domestication, learning history, and context on dog sociability.

本文研究了家犬在不同熟悉度和新颖性条件下对社会强化物（人类介导）和非社会强化物（环境）的偏好。研究人员向五只宠物狗提供了两个选择，要么是它们的主人，要么是陌生人，要么是在熟悉的环境中，要么是在不熟悉的环境中。每个场景都进一步分为被动和主动条件，这取决于人们是否试图引起注意。狗的社交能力是通过接触潜伏期、接近度和凝视率来衡量的。结果显示，不管环境是否新奇，狗始终更喜欢来自主人的社会强化。他们更快地接近主人，花更多的时间和主人在一起，对主人的凝视率通常更高。相比之下，狗对陌生人表现出较少的社交性，尤其是在新颖的环境中，这表明社会强化物的新颖性并没有增加偏好。然而，非社会新颖性确实会影响狗的行为，因为狗表现出对新环境的更多关注，而牺牲了社会互动，尤其是在陌生人面前。人类的积极行为（例如，叫狗的名字）在所有条件下都增加了社交能力，尽管大多数变化在统计上并不显著。研究结果表明，所有者社会互动的强化价值通常大于非社会新颖性，而陌生人的社会互动甚至比非社会刺激更不受欢迎。这些结果对理解狗在以人为中心的环境中的行为具有重要意义，并突出了驯化、学习历史和环境对狗的社交能力的影响。

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引用次数: 0

The effects of concept mapping experience, feedback timing, and motivation on students’ learning outcomes 概念映射经验、反馈时间和动机对学生学习成果的影响

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-08 DOI: 10.1016/j.lmot.2025.102201

Oluwafemi J. Sunday, Olusola O. Adesope, Dai Shenghai, Kira Carbonneau

Motivation plays a critical role in shaping how learners engage with instructional strategies such as concept mapping and feedback, yet its moderating influence remains underexplored. This classroom-based study investigated the impact of feedback timing and concept map experience on students’ retention and knowledge transfer in an undergraduate chemistry course. A total of 316 students were randomly assigned to one of three feedback conditions (immediate, delayed, or none) while also categorized based on their prior experience with concept mapping. The study employed a 3 × 2 factorial design. Learners completed a computer-based concept mapping task followed by assessments measuring retention and transfer. Both immediate and delayed feedback significantly enhanced knowledge transfer compared to no feedback. However, feedback timing did not significantly influence retention performance. Although not experimentally manipulated, prior concept mapping experience was associated with better performance, particularly on transfer tasks—suggesting it may support deeper knowledge application. Concept map quality strongly predicted retention, while intrinsic goal orientation emerged as a key predictor of transfer. These findings highlight how motivation and prior experience influence learning outcomes in concept mapping tasks, suggesting implications for instructional design in chemistry and related domain-specific learning environments.

动机在塑造学习者如何参与教学策略（如概念映射和反馈）方面起着关键作用，但其调节作用仍未得到充分探讨。本研究以课堂为基础，探讨了反馈时间和概念图体验对本科化学课程学生记忆和知识转移的影响。共有316名学生被随机分配到三种反馈条件中的一种（即时、延迟或没有），同时也根据他们之前的概念映射经验进行分类。本研究采用3 × 2因子设计。学习者完成了一项基于计算机的概念映射任务，随后进行了评估，以衡量记忆和转移。与无反馈相比，即时反馈和延迟反馈都显著增强了知识转移。然而，反馈时间对留存绩效没有显著影响。虽然没有实验操作，但先前的概念映射经验与更好的表现有关，特别是在迁移任务上，这表明它可能支持更深层次的知识应用。概念图质量对留存率有很强的预测作用，而内在目标取向是迁移的关键预测因素。这些发现强调了动机和先前经验如何影响概念映射任务的学习结果，这对化学和相关领域特定学习环境的教学设计提出了启示。

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引用次数: 0

Modeling Chinese second language learners' motivation, engagement, and resilience in AI-enhanced contexts: A self-determination theory 人工智能环境下中国第二语言学习者的动机、参与和弹性建模：一个自我决定理论

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-08 DOI: 10.1016/j.lmot.2025.102199

Jingjing Chen

The advance of Artificial Intelligence (AI) in the academic field has revolutionized traditional settings into innovative tools that capitalize on the presentation of learners. Chinese as a Second Language (CSL) learners' engagement is significant for their achievement in AI-enhanced contexts. Different factors can affect learners' engagement, and among them, resilience, and motivation are significant even in challenging environments. Accordingly, this study bridges the gap by suggesting a conceptual model in which resilience impacts engagement through the mediating function of motivation, analyzed through the perspectives of Self-determination theory (SDT). As a result, 630 undergraduate students from various universities in China were selected using a purposive sampling approach. The three validated questionnaires were distributed, and after collecting data, structural equation modeling (SEM) via AMOS 24.0 and SPSS 26.0 was run to examine the model. The results indicated that CSL learners' motivation, engagement, and resilience in AI-enhanced contexts are interrelated, emphasizing the interrelated nature of these psychological constructs in the path of learning. The finding prove that the resilience of learners acts as a robust predictor of their engagement, mainly through the mediating role of motivation. These results provide some implications for educators and curriculum designers in AI-enhanced contexts, emphasizing the necessity of increasing learners' motivation and resilience to optimize engagement.

人工智能（AI）在学术领域的进步彻底改变了传统的设置，使其成为利用学习者展示的创新工具。汉语作为第二语言（CSL）学习者的参与对他们在人工智能增强环境中的成就至关重要。不同的因素会影响学习者的投入，其中，弹性和动机即使在具有挑战性的环境中也很重要。因此，本研究提出了一个概念模型，通过自我决定理论（SDT）的视角分析弹性通过动机的中介作用影响敬业度，从而弥补了这一空白。因此，采用有目的的抽样方法，从中国各所大学选择了630名本科生。发放三份有效问卷，收集数据后，运用AMOS 24.0和SPSS 26.0软件进行结构方程建模（SEM），对模型进行检验。研究结果表明，在人工智能增强的情境下，CSL学习者的动机、投入和弹性是相互关联的，强调了这些心理结构在学习过程中的相互关联性质。这一发现证明，学习者的弹性主要通过动机的中介作用，作为他们投入的有力预测因子。这些结果为人工智能增强背景下的教育工作者和课程设计师提供了一些启示，强调了提高学习者的动机和适应能力以优化参与度的必要性。

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引用次数: 0

Unveiling Chinese youth students’ AI adoption goals and experiences: An achievement goal theory (AGT) perspective 中国青年学生人工智能应用目标与体验：成就目标理论视角

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-04 DOI: 10.1016/j.lmot.2025.102198

Ying Sun

Different studies have been done on various tools and technologies powered by artificial intelligence (AI) in recent years. However, the role of age groups in using and perceiving AI tools has been ignored in academic contexts. To fill the gap, this qualitative research aimed to explore Chinese youth students’ perceived AI adoption goals and experiences. A semi-structured interview was conducted with 58 general education students. The results of thematic analysis showed different AI adoption goals and experiences. The students had employed AI tools for ‘deep learning and understanding’, ‘personal growth and skill development’, ‘discovery of novel ideas and solutions’, ‘others’ outperformance and surpassing’, and ‘praise and recognition attainment’. Regarding experiences, the participants referred to four themes, namely ‘the emotional impact of AI on learners’, ‘providing a personalized and adaptive learning path’, ‘AI-mediated language learning’, and ‘the ethical considerations of AI technologies’. The findings are discussed and implications are provided for youth students, teachers, and educators to augment their AI literacy and help them set realistic goals and orientations regarding the use of AI tools.

近年来，对人工智能（AI）驱动的各种工具和技术进行了不同的研究。然而，在学术背景下，年龄群体在使用和感知人工智能工具方面的作用被忽视了。为了填补这一空白，本定性研究旨在探讨中国青年学生对人工智能采用目标和体验的感知。对58名通识教育学生进行半结构化访谈。专题分析的结果显示了不同的人工智能采用目标和经验。学生们将人工智能工具用于“深度学习和理解”、“个人成长和技能发展”、“发现新想法和解决方案”、“超越他人”以及“获得表扬和认可”。在经验方面，与会者提到了四个主题，即“人工智能对学习者的情感影响”、“提供个性化和自适应的学习路径”、“人工智能介导的语言学习”和“人工智能技术的伦理考虑”。本文对研究结果进行了讨论，并为青年学生、教师和教育工作者提供了启示，以提高他们的人工智能素养，并帮助他们在使用人工智能工具方面设定现实的目标和方向。

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引用次数: 0

Exploring enjoyment, motivation, self-efficacy, and engagement in AI-assisted English learning: A self-determination theory approach 探索人工智能辅助英语学习的乐趣、动机、自我效能和参与：一种自决理论方法

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-10-01 DOI: 10.1016/j.lmot.2025.102197

Linyan Wang , Long Wang

As artificial intelligence becomes rapidly integrated into language education, the need to understand learners’ psychological responses has grown increasingly important. However, limited research has systematically examined how emotional and motivational factors interact in AI-assisted English learning. This study investigates the relationships among enjoyment, motivation, self-efficacy, and engagement in the context of AI-assisted English learning, guided by Self-Determination Theory (SDT). A total of 840 university students participated in the study. Data analysis was carried out using SPSS 26.0 and AMOS 26.0. Structural equation modeling revealed that both enjoyment and motivation significantly predicted self-efficacy and engagement, while self-efficacy also strongly predicted engagement. Mediation analysis further confirmed that self-efficacy significantly mediated the relationships between enjoyment and engagement, as well as between motivation and engagement. These findings highlight the critical role of emotional and motivational factors in enhancing learner confidence and involvement in technology-supported language education. The results provide empirical support for applying SDT in AI-enhanced learning environments and offer practical implications for designing emotionally supportive and psychologically empowering AI tools for language learners.

随着人工智能迅速融入语言教育，了解学习者心理反应的需求变得越来越重要。然而，有限的研究系统地考察了情感和动机因素在人工智能辅助英语学习中的相互作用。本研究在自我决定理论（SDT）的指导下，探讨了人工智能辅助英语学习中享受、动机、自我效能和投入之间的关系。共有840名大学生参与了这项研究。采用SPSS 26.0和AMOS 26.0进行数据分析。结构方程模型显示，快乐和动机对自我效能感和投入有显著的预测作用，而自我效能感对投入也有显著的预测作用。中介分析进一步证实，自我效能显著中介了享受与投入、动机与投入之间的关系。这些发现强调了情感和动机因素在增强学习者信心和参与技术支持的语言教育中的关键作用。研究结果为在人工智能增强的学习环境中应用SDT提供了经验支持，并为语言学习者设计情感支持和心理授权的人工智能工具提供了实际意义。

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引用次数: 0

Predicting learner autonomy through AI-supported self-regulated learning: A social cognitive theory approach 通过人工智能支持的自我调节学习预测学习者自主性：一种社会认知理论方法

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-09-26 DOI: 10.1016/j.lmot.2025.102195

Guanghui He

As artificial intelligence (AI) becomes increasingly integrated into education, its influence on learner autonomy through self-regulated learning warrants investigation. This study examined the predictive role of AI tool usage on learner autonomy, mediated by self-efficacy, metacognitive strategies, and self-monitoring, among university students. Grounded in Social Cognitive Theory, structural equation modeling was used to analyze data from validated self-report questionnaires. Results showed that AI tool use significantly influenced learner autonomy, both directly and indirectly through psychological resources. The findings suggest that effective AI integration should not only provide technological support but also foster students’ self-regulatory capacities, contributing to the design of educational environments that encourage autonomous learning.

随着人工智能（AI）越来越多地融入教育，它通过自我调节学习对学习者自主的影响值得研究。本研究考察了人工智能工具的使用对大学生学习自主性的预测作用，以自我效能感、元认知策略和自我监控为中介。本研究以社会认知理论为基础，采用结构方程模型对自我报告问卷进行数据分析。结果表明，人工智能工具的使用通过心理资源直接和间接地影响了学习者的自主性。研究结果表明，有效的人工智能集成不仅应该提供技术支持，还应该培养学生的自我调节能力，有助于设计鼓励自主学习的教育环境。

引用次数: 0

Simulated uncertainty in gamified language learning: Investigating resilience, academic buoyancy, and on-task focus through the lens of expectancy-value theory and operant conditioning 游戏化语言学习中的模拟不确定性：通过期望值理论和操作性条件反射的视角调查弹性、学术浮力和任务焦点

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-09-26 DOI: 10.1016/j.lmot.2025.102196

Ehsan Namaziandost , Ferdi Çelik

Gamification has gained prominence in language education for its ability to enhance learner motivation and engagement, yet the role of simulated uncertainty within these environments remains underexplored. This study addresses this gap by examining how unpredictable elements in gamified language learning influence Turkish EFL learners’ resilience, academic buoyancy, and on-task focus, drawing on Expectancy-Value Theory and Operant Conditioning. A mixed-method approach was employed, involving 69 EFL students aged 16–18, randomly assigned to an experimental group (n = 36) receiving gamified instruction with simulated uncertainty or a control group (n = 33) receiving traditional non-gamified instruction without uncertainty. Quantitative data were collected using the Academic Resilience Scale, the Academic Buoyancy Scale, and an On-Task Focus Observation Checklist, while qualitative data were gathered through semi-structured interviews with 16 experimental group participants. Statistical analyses, including t-tests and ANCOVA, showed that the experimental group exhibited significantly greater improvements in resilience, buoyancy, and task-focused behavior compared to the control group, after controlling for pretest scores. Interview findings revealed that uncertain tasks fostered increased motivation, emotional regulation, and adaptive strategies, with participants noting that variable rewards and unpredictable challenges sustained their engagement and encouraged reflective learning. These results evidence that embedding simulated uncertainty in gamified instruction can enhance psychological and behavioral outcomes critical for language learning success. This study offers insights for designing motivationally supportive EFL instruction in technology-enhanced contexts.

游戏化因其增强学习者动机和参与的能力而在语言教育中获得突出地位，但模拟不确定性在这些环境中的作用仍未得到充分探讨。本研究利用期望价值理论和操作性条件反射，研究了游戏化语言学习中不可预测的因素如何影响土耳其英语学习者的弹性、学业活力和任务专注力，从而解决了这一差距。采用混合方法，涉及69名16-18岁的EFL学生，随机分配到实验组（n = 36）接受模拟不确定性的游戏化教学，对照组（n = 33）接受没有不确定性的传统非游戏化教学。定量数据采用学业弹性量表、学业浮力量表和任务焦点观察表收集，定性数据采用半结构化访谈法收集16名实验组参与者。包括t检验和ANCOVA在内的统计分析表明，在控制了前测分数后，实验组在弹性、浮力和任务集中行为方面表现出比对照组显著更大的改善。访谈结果显示，不确定的任务促进了动机、情绪调节和适应性策略的增加，参与者注意到可变的奖励和不可预测的挑战维持了他们的参与，并鼓励了反思学习。这些结果证明，在游戏化教学中嵌入模拟的不确定性可以提高对语言学习成功至关重要的心理和行为结果。本研究为在技术增强的背景下设计动机支持的英语教学提供了见解。

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引用次数: 0

Using a virtual reality resurgence task to compare time-out and extinction during alternative reinforcement 使用虚拟现实复苏任务来比较替代强化过程中的超时和消失

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-09-19 DOI: 10.1016/j.lmot.2025.102194

Rodrigo Benavides , Carlos Flores , Julian C. Velasquez , Brissa Gutiérrez , L. Rebeca Mateos

Recent studies on the resurgence of human operant behavior have failed to identify a difference in recurrence mitigation between traditional response reduction procedures (e.g. extinction) and negative reinforcement alternatives (e.g. time out). The present study employed a virtual reality-based task to enhance human ecological validity of the task while evaluating the difference between extinction and time out of the target response during a resurgence procedure. Participants were exposed to a multiple schedule consisting of alternating extinction and a time-out component for a single 10-minute session during which the target response was acquired, eliminated and tested for recurrence. Twenty participants were exposed to the procedure, which allowed the assessment of recurrence effects when participants were first exposed to either extinction or time out. No statistically significant difference was found in resurgence mitigation between participants regardless of the order of presentation. This work highlights the methodological innovation of embedding extinction and time-out procedures within a virtual reality environment, offering a promising framework for studying complex behavioral processes. Future directions are suggested to refine virtual reality-based procedures.

最近关于人类操作行为复苏的研究未能确定传统反应减少程序（如灭绝）和负强化替代方案（如暂停）在缓解复发方面的差异。本研究采用基于虚拟现实的任务来提高任务的人类生态有效性，同时评估在复苏过程中目标反应的灭绝和时间的差异。参与者被暴露在一个由交替消失和暂停组成的多重计划中，在一个10分钟的会议中，目标反应被获得，消除和测试复发。20名参与者接受了这个程序，当参与者第一次暴露于灭绝或暂停时，可以评估复发效应。无论呈现的顺序如何，受试者之间的死灰复燃缓解没有统计学上的显著差异。这项工作强调了在虚拟现实环境中嵌入消光和超时程序的方法创新，为研究复杂的行为过程提供了一个有前途的框架。建议未来的发展方向是完善基于虚拟现实的程序。

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引用次数: 0

The role of AI-driven feedback in fostering growth mindset and engagement: A self-determination theory perspective 人工智能驱动的反馈在培养成长心态和参与方面的作用：一个自决理论的视角

IF 1.8 4区心理学 Q3 PSYCHOLOGY, BIOLOGICAL

Learning and Motivation

Pub Date : 2025-09-17 DOI: 10.1016/j.lmot.2025.102192

Yu Quan

This study investigates the motivational processes through which AI-driven feedback fosters a growth mindset, learner engagement, and persistence, framed by self-determination theory (SDT). A sample of 507 undergraduate students interacted with an AI tutoring system over six weeks, providing ratings on feedback quality, psychological need satisfaction, growth mindset endorsement, engagement, and persistence. Data integrity checks, reliability analyses, and descriptive statistics were conducted in IBM SPSS Statistics 27, followed by confirmatory factor analyses and structural equation modeling in AMOS 24. Results showed high mean ratings across all constructs and acceptable measurement-model fit. Structural modeling revealed that perceived AI feedback quality exerted strong direct effects on engagement and persistence. Additionally, AI feedback influenced both outcomes indirectly through two motivational pathways: intrinsic motivation and learner autonomy. All indirect effects were statistically significant (p < .01), underscoring the dual mediators by which algorithmic feedback supports self-regulated learning. These findings extend SDT and growth mindset theories into digital learning environments, demonstrating that AI feedback can satisfy core psychological needs and activate adaptive motivational beliefs. Practical implications include designing AI systems with autonomy-supportive prompts and competence-affirming messages. Future research should explore enhanced social-affective cues in AI feedback and longitudinal effects on academic outcomes.

本研究在自我决定理论（SDT）的框架下，探讨了人工智能驱动的反馈培养成长型思维、学习者参与度和持久性的激励过程。在为期六周的时间里，507名本科生与人工智能辅导系统进行了互动，对反馈质量、心理需求满意度、成长心态认可、参与度和持久性进行了评分。在IBM SPSS statistics 27中进行数据完整性检查、信度分析和描述性统计，然后在AMOS 24中进行验证性因子分析和结构方程建模。结果显示，在所有结构和可接受的测量模型拟合中，平均评分较高。结构模型显示，感知到的AI反馈质量对用户粘性和持久性有着强烈的直接影响。此外，人工智能反馈通过两种激励途径间接影响了这两种结果：内在动机和学习者自主。所有间接效应均有统计学意义（p <； ）。01)，强调了算法反馈支持自我调节学习的双重中介。这些发现将SDT和成长心态理论扩展到数字学习环境中，表明人工智能反馈可以满足核心心理需求并激活适应性动机信念。实际影响包括设计具有自主支持提示和能力确认信息的人工智能系统。未来的研究应该探索人工智能反馈中增强的社会情感线索以及对学业成绩的纵向影响。

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引用次数: 0