Position AI chatbots for relatedness as companions that normalize help-seeking and connect students to human communities, not substitutes for them¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Because students with lower baseline relatedness perceived more relatedness support from chatbots, the authors identify a design opportunity: "AI chatbots may be most valuable for relatedness when they help students feel accompanied, normalize help-seeking, or connect them back to human learning communities, rather than becoming a substitute for those communities." This positions chatbot social support as a bridge to, not a replacement for, peer and instructor relationships, most salient for students beginning with weaker feelings of connection.

Design Implications¶

Context¶

Requirements¶

• Design and user expectations that evoke rapport and social presence, which prior HCI work the article cites says relational experiences depend strongly on

Constraints¶

• The negative baseline-relatedness path should not be interpreted as evidence that chatbots can replace peer, instructor, or classroom relationships

Target Learners¶

• higher education students, especially those with weaker initial feelings of connection

Target Learning Goals¶

• relatedness need support and normalized help-seeking

Related Strategies¶

• Design needs- and attention-aware AI learning tools using mechanism-level measures, heterogeneous-effects analysis, and in-process telemetry
• Culturally tailored chatbot design: position chatbots as reflective companions for Chinese users and as thinking partners for Western users, while deploying cognitive support broadly

Examples¶

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Key Sources¶

• Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous. (2026). Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. arXiv. https://arxiv.org/abs/2607.26338