人工智能辅助学习：聊天机器人新颖性和个体创新性的匹配与学习投入的关系

doi:10.12139/j.1672-0628.2026.04.012

摘要/Abstract

摘要：

使用人工智能(AI)聊天机器人辅助学习被证实对学习投入有积极作用，但对相关影响因素缺乏深入研究。本研究基于人−环境匹配理论和自我决定理论，通过响应面分析建模的方式探讨聊天机器人新颖性和个体创新性的匹配与学习投入的关系。以511名在校大学生为被试，使用问卷评估感知聊天机器人新颖性、个体创新性和学习投入。结果表明：(1)聊天机器人新颖性与个体创新性越一致，学习投入程度越高；(2)相对于聊天机器人新颖性和个体创新性均低，二者均高时个体学习投入程度更高；(3)相对于聊天机器人新颖性低于个体创新性，前者高于后者时学习投入程度更高。本研究不仅丰富了相关主题研究，还为人工智能时代如何有效利用智能聊天机器人辅助学习提供了实证依据。

关键词: 人工智能, 聊天机器人新颖性, 个体创新性, 学习投入, 响应面分析

Abstract:

Although AI chatbots have demonstrated positive effects on students’ learning engagement, the underlying factors that govern these benefits remain underspecified. Guided by person-environment fit and self-determination theories, this study employed response surface analysis to explore the relationship between individual innovativeness, chatbot novelty, and learning engagement. A sample of 511 college students participated in the study, with questionnaires assessing individual innovativeness, perceived chatbot novelty, and learning engagement. The results revealed that: 1) engagement increased as perceived novelty and innovativeness converged; 2) engagement was higher when both dimensions were high rather than low; and 3) learning engagement was greater when chatbot novelty exceeded individual innovativeness than in the reverse scenario. These findings extend the chatbot-education literature and provide evidence-based guidance for optimizing AI chatbots to foster engaged learning in the digital era.

Key words: artificial intelligence, chatbot novelty, individual innovativeness, learning engagement, response surface analysis

中图分类号:

B849

罗星雨, 毛佩佩, 牛更枫, 翟培培, 孙晓军. 人工智能辅助学习：聊天机器人新颖性和个体创新性的匹配与学习投入的关系[J]. 心理与行为研究, 2026, 24(4): 525-532.

Xingyu LUO, Peipei MAO, Gengfeng NIU, Peipei ZHAI, Xiaojun SUN. AI-Assisted Learning: The Relationship Between Chatbot Novelty - Individual Innovativeness Congruence and Learning Engagement[J]. Studies of Psychology and Behavior, 2026, 24(4): 525-532.

图/表 5

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模型	参数约束	模型解释
简单一致性上升岭模型	b1=b2; b3=b5; b3+b4+b5=0; b6=0; b7=0; b8=0; b9=0	对于具有相同平均预测变量水平(x+y)/2的个体，x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值(x−y)的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]。
水平依赖一致性上升岭模型	b1=b2; b3=b5; b3<0; b4=−2b3; b7=−b6; b8=−b6; b9=b6	对于具有相同平均预测变量水平(x+y)/2的个体，x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值x−y的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]；对于四名个体A、B、C和D，其中A和B以及C和D分别具有相同的平均预测变量水平(x+y)/2，A和B的平均预测变量水平低于C和D，且A和C以及B和D分别具有相同的差异值x−y，A和B的z值绝对差异大于(或小于)C和D的z值绝对差异[水平依赖效应]。
非对称一致性上升岭模型	b1=b2; b3=b5; b3<0; b4=−2b3; b7=−3b6; b8=3b6; b9=−b6	对于两组个体，若每组内部的x和y值要么都大于或小于对方，且具有相同的平均预测变量水平(x+y)/2，则x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值x−y的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]；对于平均预测变量水平[(x+y)/2]相同且差异值(x−y)大小相等但符号相反的个体，x小于y的个体z值更高(或更低)[非对称效应]。

模型	参数约束	模型解释
简单一致性上升岭模型	b1=b2; b3=b5; b3+b4+b5=0; b6=0; b7=0; b8=0; b9=0	对于具有相同平均预测变量水平(x+y)/2的个体，x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值(x−y)的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]。
水平依赖一致性上升岭模型	b1=b2; b3=b5; b3<0; b4=−2b3; b7=−b6; b8=−b6; b9=b6	对于具有相同平均预测变量水平(x+y)/2的个体，x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值x−y的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]；对于四名个体A、B、C和D，其中A和B以及C和D分别具有相同的平均预测变量水平(x+y)/2，A和B的平均预测变量水平低于C和D，且A和C以及B和D分别具有相同的差异值x−y，A和B的z值绝对差异大于(或小于)C和D的z值绝对差异[水平依赖效应]。
非对称一致性上升岭模型	b1=b2; b3=b5; b3<0; b4=−2b3; b7=−3b6; b8=3b6; b9=−b6	对于两组个体，若每组内部的x和y值要么都大于或小于对方，且具有相同的平均预测变量水平(x+y)/2，则x和y值更接近的个体z值更高(或更低)[一致性效应]；对于具有相同差异值x−y的个体，平均预测变量水平(x+y)/2较高的个体z值更高(或更低)[线性上升岭效应]；对于平均预测变量水平[(x+y)/2]相同且差异值(x−y)大小相等但符号相反的个体，x小于y的个体z值更高(或更低)[非对称效应]。

变量	M	SD	1	2	3	4
1.性别
2.年龄	22.21	2.76
3.聊天机器人新颖性	5.68	0.84	0.02	−0.02
4.个体创新性	5.41	0.96	0.23**	0.03	0.46***
5.学习投入	5.37	0.96	0.11*	0.004	0.65***	0.55***

变量	M	SD	1	2	3	4
1.性别
2.年龄	22.21	2.76
3.聊天机器人新颖性	5.68	0.84	0.02	−0.02
4.个体创新性	5.41	0.96	0.23**	0.03	0.46***
5.学习投入	5.37	0.96	0.11*	0.004	0.65***	0.55***

模型	k	AIC	ΔAIC	Log-Likelihood	CFI	SRMR	RMSEA	R²
RRCA	3	1062.35	0.00	−526.11	0.99	0.006	0.027	0.513
FULL	9	1066.60	4.26	−522.04	1.00	0.000	0.000	0.521
RRCL	3	1067.63	5.29	−528.76	0.98	0.008	0.049	0.508
RRSC	2	1073.10	10.76	−532.51	0.96	0.015	0.062	0.501
NULL	0	1423.93	361.58	−709.95	0.00	0.161	0.282	0.000