Proxemic configuration significantly shifts the relational organization of regulation, from a visually anchored system when far to a strategy-oriented system when close¶

Claim · All claims
Evidence · 1 study · associational r1 · q3 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 Mann–Whitney testing showed prox_close groups occupied higher relational scores than prox_far groups along RR1 (U = 77.00, p = 0.01, r = 0.52), with no difference along SVD2 (U = 138.00, p = 0.46, r = 0.15). → Ridwan Whitehead · Andy Nguyen · Sanna Järvelä 2026

Evidence¶

Ridwan Whitehead · Andy Nguyen · Sanna Järvelä 2026¶

Ridwan Whitehead · Andy Nguyen · Sanna Järvelä. (2026). How gaze and proxemics shape and signal shared regulatory processes during AI-supported collaborative learning. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-026-09478-7

q3 · i3 · associational · r1

Mann–Whitney analyses supporting the visual interpretation of the prox_close versus prox_far TONA networks in the RQ2 results, reporting "prox_close groups (Mdn = 0.13) occupied higher relational scores than prox_far groups (Mdn = −0.09)" with r = 0.52 along RR1.

"A significant difference along RR1 (U = 77.00, p = 0.01, r = 0.52), showed that prox_close groups (Mdn = 0.13) occupied higher relational scores than prox_far groups (Mdn = −0.09). No difference emerged along SVD2 (U = 138.00, p = 0.46, r = 0.15)."

Discussion¶

Related Claims¶

• When groups are physically far apart, mutual gaze forms the dominant pathway into monitoring, and behavioral challenges dominate — related
• Adaptive AI support directs regulatory activity toward monitoring while static AI support produces strategy-focused, procedural regulation — related