成就目标导向如何影响大学生用户的AI助教工具使用行为？——基于双重中介的实证研究

doi:10.13998/j.cnki.issn1002-1248.25-0399

Abstract

Abstract:

[Purpose/Significance] This study addresses the "motivation black box" problem. By integrating achievement goal theory and technology acceptance models, it aims to construct a four-dimensional "motivation-identity-cognition-engagement" theoretical framework to analyze the driving mechanisms underlying AI teaching assistant usage behavior. [Method/Process] A questionnaire survey was utilized in this study. The Chaoxing Learning platform served as the research context, and college students who use AI teaching assistants constitute the research subjects. The chain mediating effect between technical identity recognition and technical acceptance was tested using the structural equation modeling (SEM). The significance of the pathways was verified via the Bootstrap sampling method. Data analysis was performed using SPSS 26.0 and Smart PLS 3.3.9 software. [Results/Conclusions] Key findings reveal that within the learning environment integrating Chaoxing's online courses with AI teaching assistants, achievement goal orientations demonstrated significant divergence, with mastery-approach goals (MAP) emerging as the sole significant driver - other goal orientations showed no statistically reliable predictive effects. Crucially, MAP significantly promoted dependent (β=0.308), critical (β=0.262), and exploratory (β=0.244) usage behaviors through the "technology identity recognition → technology acceptance" chain-mediation pathway. Furthermore, technology identity recognition exhibited dual mediation dominance in behavior formation, as this chain-mediation pathway accounted for more than 50% of total effects across all three usage behaviors, particularly for dependent and exploratory usage. Notably, technology identity recognition demonstrated the strongest mediation effect specifically on dependent behaviors (β=0.418). Further analysis indicates MAP's total effect on technology identity recognition substantially exceeded its direct effect on technology acceptance. This critical finding aligns with Deci and Ryan's self-determination theory, confirming that intrinsic motivation (exemplified by MAP) facilitates deeper skill internalization. Specifically, students focused on competence development showed greater tendency to integrate AI skills into their self-concept (e.g., perceiving themselves as "technology-proficient learners") rather than viewing them merely as external tools - a mechanism that empirically explains why traditional technical training that emphasizes operational skills often fails to foster sustained usage. Most significantly, this research provides important implications for educators in guiding students' use of AI teaching assistants: they should prioritize cultivating students' mastery-approach goals (MAP) through instructional design that strengthens students' pursuit of knowledge. Such an approach enhances the effectiveness of AI tools in teaching while simultaneously offering direction for the Chaoxing Learning Platform to optimize its AI teaching assistant features. Specifically, the platform should enhance personalized learning support tailored to the needs of MAP-oriented users, thereby better aligning with students' intrinsic learning motivations.

Key words: achievement goal theory, technical identity, technical acceptance, use of AI tools, AI literacy

CLC Number:

G350

ZHANG Tao, WU Sihang. How Achievement Goal Orientation Influences College Students' Usage Behaviors of AI Tutoring Tools: An Empirical Study Based on Dual Mediation[J].Journal of library and information science in agriculture, 2025, (): 1-15.

Figures/Tables 10

Fig.1

Table1

Variables and measurement items"

测量变量	题项序号	测量题项	文献来源
掌握-接近目标（Mastery-Approach）	MAP1	我使用AI助教的目标是彻底掌握课程内容	[6]
	MAP2	我会通过AI助教深入理解这门课的知识
	MAP3	我希望用AI助教探索课程中的复杂问题
掌握-回避目标（Mastery-Avoidance）	MAV1	我使用AI助教是为了避免学不会课程重点
	MAV2	我担心不用AI助教会遗漏重要知识点
	MAV3	我会通过AI助教反复练习，防止自己学得不扎实
表现-接近目标（Performance-Approach）	PAP1	我使用AI助教是为了比其他同学表现更好
	PAP2	我会通过AI助教争取在考试中得高分
	PAP3	我希望用AI助教证明自己比他人更优秀
表现-回避目标（Performance-Avoidance）	PAV1	我使用AI助教是为了避免成绩比其他同学差
	PAV2	我担心不用AI助教会表现得更差
	PAV3	我会依赖AI助教防止自己在班级中落后
技术接受度（Technology Acceptance）	TA1	我觉得AI助教的操作界面简单易懂	[33]
	TA2	学习使用AI助教不需要花费太多精力
	TA3	AI助教的功能布局直观，易于导航
	TA4	我能轻松找到AI助教的功能入口
	TA5	AI助教能准确解答我的学习问题
	TA6	AI助教的互动方式让学习过程更生动
	TA7	我计划在下学期继续使用AI助教。
技术身份认同（Identity Technology）	IT1	我认为AI助教是我的学习伙伴（而不仅仅是工具）	[24]
	IT2	我在学习过程中会主动向AI助教寻求建议和帮助
	IT3	使用AI助教让我感觉像在与真人交流
	IT4	我会记住AI助教的回答风格和特点
探索性使用（Exploratory Use）	EU1	我会调整AI助教的参数（如响应长度、语气）以获得更好结果	[34]
	EU2	我会将AI助教应用于其他课程或生活场景
	EU3	我会尝试AI助教的所有功能，即使课程未要求
	EU4	我经常用AI助教探索课程外的知识（如延伸阅读）
批判性使用（Critical Use）	CU1	当AI助教首次无法解答时，我会尝试不同提问方式	[35]
	CU2	当AI助教的答案与我的理解冲突时，我会主动查证
	CU3	使用AI助教后，我会更注意检查信息的来源可靠性
	CU4	我能更快速地评估AI推荐资源的可靠性
依赖性使用（Dependent Use）	DU1	即使没有教师要求，我也会主动使用AI助教	[36]
	DU2	我会优先选择AI助教而非传统搜索工具解决学习问题
	DU3	我会向其他同学推荐使用AI助教

Table1

Fig.2

Fig.3

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

References 43

[1]	詹希旎, 李白杨. 生成式人工智能(GAI)背景下的数智可供性与认知带宽调节研究[J]. 图书与情报, 2024(1): 110-120.
	ZHAN X N, LI B Y. Research on data intelligence affordance and cognitive bandwidth adjustment in the context of generative artificial intelligence(GAI)[J]. Library & information, 2024(1): 110-120.
[2]	李思睿, 侯婧羽. ChatGPT用户使用行为的影响因素研究[J]. 传播创新研究, 2025(1): 118-137, 231.
	LI S R, HOU J Y. Research on the influencing factors of ChatGPT user behavior[J]. Communication innovation research, 2025(1): 118-137, 231.
[3]	罗国锋, 刘清生. ChatGPT赋能高校信息素养教育应用场景与实践研究[J]. 农业图书情报学报, 2024, 36(4): 91-101.
	LUO G F, LIU Q S. Application scene and practice of ChatGPT empowering information literacy education[J]. Journal of library and information science in agriculture, 2024, 36(4): 91-101.
[4]	吴佳桧, 傅海伦. 成就目标定向与学业成绩关系的元分析: 自我效能感、学习投入的中介作用[J]. 心理科学进展, 2024, 32(7): 1104-1131.
	WU J H, FU H L. A meta-analysis of the relationship between achievement goal orientation and academic achievement: The mediating role of self-efficacy and student engagement[J]. Advances in psychological science, 2024, 32(7): 1104-1131.
[5]	范哲. 基于用户画像的数字原住民社会化媒体采纳意愿的阶段性分析[J]. 现代情报, 2017, 37(6): 99-106.
	FAN Z. The stage analysis about digital natives' adoption intentions of social media based on person[J]. Journal of modern information, 2017, 37(6): 99-106.
[6]	SAHNA SREEN A H, MAJID M H M. Leveraging ChatGPT for personalized learning: A systematic review in educational settings[J]. Amandemen: Journal of Learning, Teaching and Educational Studies, 2024, 2(1): 63-78.
[7]	侯浩翔, 管培俊. 生成式人工智能时代的高校整体性治理[J]. 江苏高教, 2025(6): 1-10.
	HOU H X, GUAN P J. Overall governance of universities in the era of generative artificial intelligence[J]. Jiangsu higher education, 2025(6): 1-10.
[8]	GAŠEVIĆ D, SIEMENS G, SADIQ S. Empowering learners for the age of artificial intelligence[J]. Computers and education: Artificial intelligence, 2023, 4: 100130.
[9]	陈昂轩, 贾积有. 可解释性人工智能有助于提升自适应学习的学习效果吗?: 基于29项实验与准实验的元分析[J]. 现代教育技术, 2024, 34(10): 92-102.
	CHEN A X, JIA J Y. Does the explainable artificial intelligence help enhance the learning outcomes of adaptive learning? : Meta-analysis based on 29 experiments and quasi-experiments[J]. Modern educational technology, 2024, 34(10): 92-102.
[10]	SPARKS J R, KATZ I R, BEILE P M. Assessing digital information literacy in higher education: A review of existing frameworks and assessments with recommendations for next-generation assessment[J]. ETS research report series, 2016, 2016(2): 1-33.
[11]	WISMATH S L, ORR D. Collaborative learning in problem solving: A case study in metacognitive learning[J]. The Canadian journal for the scholarship of teaching and learning, 2015, 6(3): 10.
[12]	ELLIOT A J, MCGREGOR H A. A 2 × 2 achievement goal framework[J]. Journal of personality and social psychology, 2001, 80(3): 501-519.
[13]	SHARMA K, GIANNAKOS M, DILLENBOURG P. Eye-tracking and artificial intelligence to enhance motivation and learning[J]. Smart learning environments, 2020, 7(1): 13.
[14]	DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS quarterly, 1989, 13(3): 319.
[15]	黄成立. 大学生AIGC利用现状与高校图书馆服务的实证分析[J]. 情报探索, 2025(2): 119-126.
	HUANG C L. Empirical analysis of college students' utilization of AIGC and university library services[J]. Information research, 2025(2): 119-126.
[16]	郭亚军, 刘振阳, 郭一若, 等. AIGC大学生用户信息需求研究: 以ChatGPT为例[J]. 情报科学, 2024, 42(8): 34-44.
	GUO Y J, LIU Z Y, GUO Y R, et al. Study on the information needs of college students using AIGC: Taking ChatGPT as an example[J]. Information science, 2024, 42(8): 34-44.
[17]	徐顺, 田潇湘. 基于TAM的在线学习平台用户使用意愿影响因素研究[J]. 中国教育信息化, 2020, 26(8): 78-85.
	XU S, TIAN X X. Research on influencing factors of users' intention to use online learning platform based on TAM[J]. The Chinese journal of ICT in education, 2020, 26(8): 78-85.
[18]	张敏, 祝安, 张芳. 在线教育平台用户持续使用行为研究的影响因素[J]. 图书馆论坛, 2020, 40(5): 82-91.
	ZHANG M, ZHU A, ZHANG F. Research on influencing factors of online education platform users' continuous use behavior[J]. Library tribune, 2020, 40(5): 82-91.
[19]	吴冰, 王毓芳. 移动健身APP持续使用的影响因素研究[J]. 软科学, 2019, 33(10): 87-92.
	WU B, WANG Y F. Research on the influencing factors of continuous use of mobile fitness APP[J]. Soft science, 2019, 33(10): 87-92.
[20]	常桂林, 毕强, 费陆陆. 微信平台(公众号)用户持续使用意愿分析: 基于期望确认模型与媒介系统依赖理论[J]. 图书馆学研究, 2017(22): 85-92.
	CHANG G L, BI Q, FEI L L. An analysis of users' continuance intention to use WeChat public platform: Based on the expectation confirmation model and media system dependency theory[J]. Research on library science, 2017(22): 85-92.
[21]	ALI I, WARRAICH N F, BUTT K. Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction[J]. Information development, 2025, 41(3): 859-874.
[22]	HWANG Y, YI M Y. Predicting the use of web-based information systems: Intrinsic motivation and self-efficacy[J]. AMCIS 2002 Proceedings, 2002: 149.
[23]	CARTER M, GROVER V, et al. Me, myself, and I(T): Conceptualizing information technology identity and its implications[J]. MIS quarterly, 2015, 39(4): 931-957.
[24]	CARTER M, PETTER S, GROVER V, et al. Information technology identity: A key determinant of IT feature and exploratory usage[J]. MIS quarterly, 2020, 44(3): 983-1021.
[25]	OGBANUFE O, GERHART N. The mediating influence of smartwatch identity on deep use and innovative individual performance[J]. Information systems journal, 2020, 30(6): 977-1009.
[26]	CHEN K, Alan Hoi Shou Chan. Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM) [J]. Ergonomics, 2014, 57(5): 635-652.
[27]	SUNDAR S S, BELLUR S, OH J, et al. Theoretical importance of contingency in human-computer interaction[J]. Communication research, 2016, 43(5): 595-625.
[28]	GUALTIERI L, RAUCH E, VIDONI R. Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review[J]. Robotics and computer-integrated manufacturing, 2021, 67: 101998.
[29]	DECI E L, RYAN R M. The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior[J]. Psychological inquiry, 2000, 11(4): 227-268.
[30]	GILLIER T, PIAT G. Exploring over: The presumed identity of emerging technology[J]. Creativity and innovation management, 2011, 20(4): 238-252.
[31]	VENKATESH, THONG, XU. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology[J]. MIS quarterly, 2012, 36(1): 157.
[32]	PINTRICH P R. Multiple goals, multiple pathways: The role of goal orientation in learning and achievement[J]. Journal of educational psychology, 2000, 92(3): 544-555.
[33]	VENKATESH V, DAVIS F D. A theoretical extension of the technology acceptance model: Four longitudinal field studies[J]. Management science, 2000, 46(2): 186-204.
[34]	BLISS J, OGBORN J. Tools for exploratory learning: A research programme[J]. Journal of computer assisted learning, 1989, 5(1): 37-50.
[35]	HUANG C-W, COLEMAN M, GACHAGO D, et al. Using ChatGPT to encourage critical AI literacy skills and for assessment in higher education[M]//ICT Education. Cham: Springer Nature Switzerland, 2023: 105-118.
[36]	PRATIWI H, RIWANDA A, HASRUDDIN H, et al. Transforming learning or creating dependency teachers perspectives and barriers to AI integration in education[J]. Journal of pedagogical research, 2025: 2.
[37]	HAIR J F, RISHER J J, SARSTEDT M, et al. When to use and how to report the results of PLS-SEM[J]. European business review, 2019, 31(1): 2-24.
[38]	NUNNALLY J C. Psychometric theory: 25 years ago and now[J]. Educational researcher, 1975, 4(10): 7-21.
[39]	FORNELL C, LARCKER D F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of marketing research, 1981, 18(1): 39.
[40]	PREACHER K J, HAYES A F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models[J]. Behavior research methods, 2008, 40(3): 879-891.
[41]	刘琼, 刘星, 刘桂锋. “人工智能+高等教育”应用场景下的AI素养框架研究[J]. 农业图书情报学报, 2024, 36(8): 43-55.
	LIU Q, LIU X, LIU G F. Framework of AI literacy for multi-role capabilities in future education: Based on typical cases of "AI+Higher education" application scenarios[J]. Journal of library and information science in agriculture, 2024, 36(8): 43-55.
[42]	李白杨, 唐昆. AIGC背景下全民数字素养教育的内涵变革与应对策略[J]. 图书与情报, 2024(3): 32-39.
	LI B Y, TANG K. Connotation change and countermeasures of digital literacy education for all in the context of AIGC[J]. Library & information, 2024(3): 32-39.
[43]	赵一鸣, 郑乔治, 沈校亮. 虚拟数字人对用户在线学习效果的影响研究[J]. 现代情报, 2025, 45(1): 81-96.
	ZHAO Y M, ZHENG Q Z, SHEN X L. Research on the influence of virtual digital human on users' online learning performance[J]. Journal of modern information, 2025, 45(1): 81-96.

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潜变量	CA	CR	（AVE）
掌握-接近目标（MAP）	0.854	0.911	0.774
技术接受度	0.929	0.943	0.703
技术身份认同	0.84	0.892	0.674
探索性使用	0.838	0.891	0.672
依赖性使用	0.821	0.894	0.737
批判性使用	0.86	0.906	0.707

潜变量	掌握-接近目标	依赖性使用	批判性使用	技术接受度	技术身份认同	探索性使用
掌握-接近目标	0.880
依赖性使用	0.696	0.859
批判性使用	0.768	0.736	0.841
技术接受度	0.709	0.856	0.728	0.839
技术身份认同	0.737	0.749	0.749	0.746	0.821
探索性使用	0.751	0.660	0.792	0.678	0.755	0.820

潜变量测度项	掌握-接近目标（MAP）	探索性使用（EU）	批判性使用（CU）	依赖性使用（DU）	技术身份认同（IT）	技术接受度（TA）
MAP1	0.801
MAP2	0.924
MAP3	0.909
EU1		0.737
EU2		0.865
EU3		0.792
EU4		0.878
CU1			0.846
CU2			0.914
CU3			0.837
CU4			0.759
DU1				0.895
DU2				0.795
DU3				0.882
IT1					0.857
IT2					0.815
IT3					0.777
IT4					0.832
TA1						0.831
TA2						0.780
TA3						0.886
TA4						0.854
TA5						0.802
TA6						0.832
TA7						0.880

路径	路径系数	标准差	T值
MAP->技术接受度	0.350	0.075	4.683
MAP->技术身份认同	0.737	0.054	13.745
技术接受度->依赖性使用	0.856	0.045	19.173
技术接受度->批判性使用	0.728	0.055	13.274
技术接受度->探索性使用	0.678	0.051	13.228
技术身份认同->技术接受度	0.488	0.088	5.532

路径	路径系数	标准差	T值	95%CI
MAP->技术接受度->探索性使用	0.237	0.059	4.026	[0.139,0.368]
MAP->技术接受度->依赖性使用	0.299	0.066	4.553	[0.180,0.437]
MAP->技术接受度->批判性使用	0.254	0.062	4.107	[0.148,0.388]
MAP->技术身份认同->技术接受度	0.36	0.065	5.496	[0.223,0.483]
技术身份认同->技术接受度->依赖性使用	0.418	0.081	5.181	[0.248,0.563]
技术身份认同->技术接受度->批判性使用	0.355	0.071	4.967	[0.211,0.492]
技术身份认同->技术接受度->探索性使用	0.331	0.065	5.067	[0.201,0.457]
MAP->技术身份认同->技术接受度->探索性使用	0.244	0.051	4.816	[0.148,0.346]
MAP->技术身份认同->技术接受度->批判性使用	0.262	0.055	4.785	[0.156,0.374]
MAP->技术身份认同->技术接受度->依赖性使用	0.308	0.062	4.962	[0.183,0.425]

How Achievement Goal Orientation Influences College Students' Usage Behaviors of AI Tutoring Tools: An Empirical Study Based on Dual Mediation

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路径	路径系数	标准差	T值
MAP->依赖性使用	0.607	0.06	10.171
MAP->批判性使用	0.516	0.065	7.983
MAP->探索性使用	0.481	0.061	7.926
MAP->技术接受度	0.709	0.047	15.153
MAP->技术身份认同	0.737	0.054	13.745
技术接受度->依赖性使用	0.856	0.045	19.173
技术接受度->批判性使用	0.728	0.055	13.274
技术接受度->探索性使用	0.678	0.051	13.228
技术身份认同->依赖性使用	0.418	0.081	5.181
技术身份认同->批判性使用	0.355	0.071	4.967
技术身份认同->技术接受度	0.488	0.088	5.532
技术身份认同->探索性使用	0.331	0.065	5.067

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