MIMIC model regression paths and DIF Effects.

The rapid adoption of generative AI in higher education raises critical questions about its impact on student motivation and basic psychological needs. This study introduces and validates the AI-Motivation and Needs (AIM-N) scale, a new instrument assessing how AI integration influences students’ motivational orientations and need satisfaction in learning. Survey data were collected from N = 904 university students. A confirmatory factor analysis (CFA) supported a multi-factor structure for the AIM-N, comprising two subscales of AI-related redundancy beliefs (task-level and motivational-level) and three subscales of AI-related motivational orientations (intrinsic, identified, controlled), with acceptable model fit (CFI ≈ 0.96, TLI ≈ 0.95, RMSEA ≈ 0.05) and strong factor loadings. Internal consistency was good for most subscales (Cronbach’s α = 0.70–0.90; McDonald’s ω in similar range), except a single-item amotivation indicator. Multi-group CFA indicated that the AIM-N achieved configural, metric, and scalar invariance across gender, study level (Bachelor’s, Master’s, PhD), academic field, and frequency of AI use (ΔCFI生成式AI（generative AI）在高等教育领域的快速普及，引发了诸多关于其对学生动机与基本心理需求影响的关键议题。本研究介绍并验证了AI动机与需求（AI-Motivation and Needs, AIM-N）量表——一款全新的测评工具，用于评估人工智能整合如何影响学生的学习动机取向与需求满足状态。研究共收集了N=904名大学生的调查数据。验证性因素分析（confirmatory factor analysis, CFA）结果支持AIM-N量表具备多因子结构：该量表包含两个人工智能相关冗余信念分量表（任务层面与动机层面），以及三个人工智能相关动机取向分量表（内在型、认同型、控制型）；其模型拟合度良好（CFI≈0.96，TLI≈0.95，RMSEA≈0.05），因子载荷表现优异。除单条目无动机指标外，多数分量表的内部一致性表现良好（克朗巴赫α系数为0.70~0.90；麦克唐纳ω系数处于相近区间）。多组验证性因素分析结果显示，AIM-N量表在性别、学业层次（本科、硕士、博士）、学科领域以及人工智能使用频率等群体间均实现了构型不变性、度量不变性与严格不变性（ΔCFI