#994: Linguistic Cues in Instructor and AI Feedback: Linking Language to Student Motivation

This study examined how linguistic cues in instructor feedback relate to students’ motivation and how these patterns differ between human and AI-facilitated feedback. Feedback from 123 undergraduates in an introductory educational psychology course was analyzed using the General Inquirer and Lasswell dictionaries. Correlations between linguistic features and students’ perceived attainment, interest, and utility values revealed that cognitively rich and action-oriented language (e.g., problem-solving, active verbs) enhanced motivation, while doctrinal and ideological language reduced it. Relational cues expressing affiliation fostered interest value, whereas loss-focused phrasing undermined attainment value. Comparisons showed that AI feedback used more cognitive and affective expressions but also more abstract ideological framing. These findings highlight the motivational importance of feedback tone: effective feedback should be confident yet collaborative and specific yet empathetic. Designing AI systems that balance analytical precision with relational authenticity may better sustain student motivation in feedback-rich environments.