Tutoring with Humans and Machines: Recommendations for Institutional Practice

Daniel Sanford; Leif Nelson; Anders Tobiason

Tutoring with Humans and Machines: Recommendations for Institutional Practice

Boise State University

Research output: Working paper

Abstract

The central recommendation is that institutions preserve and strengthen peer tutoring, which provides academic, developmental, and relational benefits that AI cannot replicate. AI tutoring can improve learning, but only when framed and supervised within human-guided, pedagogically grounded models. The paper urges institutions to adopt AI cautiously, given risks related to accuracy, opacity, bias, privacy, accessibility, and overreliance. AI may supplement practice or help meet unmet demand, but it should not replace human interaction. Institutions should differentiate AI tutors from chatbots, establish ethical and pedagogical oversight, and ensure that technology use reinforces rather than erodes the human foundations of learning.

Original language	American English
Number of pages	9
State	Published - 5 Dec 2025

Cite this

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Tutoring with Humans and Machines: Recommendations for Institutional Practice

Abstract

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