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arxiv: 2605.04816 · v2 · pith:7KBVAEJNnew · submitted 2026-05-06 · 💻 cs.HC

Building AI Companions that Prioritise Learning over Performance

Hassan Khosravi , Dragan Gasevic , Shazia Sadiq , Lixiang Yan , Jason Lodge , Jason Tangen , Paul Denny , Kristen DiCerbo
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Simon Buckingham Shum Ryan S. Baker
This is my paper

Pith reviewed 2026-05-19 17:34 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI learning companionslearning-performance paradoxpedagogical designadaptive AIeducational technologymetacognitive growthlearner agencyLLM in education
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The pith

AI in education must move from performance-boosting LLMs to deliberately designed learning companions that prioritize durable understanding.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies a learning-performance paradox in which LLMs improve short-term task outputs like writing or coding yet can weaken cognitive growth, knowledge transfer, and metacognitive skills. It proposes AI learning companions as adaptive, LLM-powered agents built specifically for learning environments rather than general performance. A framework with three foundations guides their design: a pedagogical one that addresses how students learn with AI, an adaptive one that lets the AI learn about the student, and a responsible one that enforces transparency, accountability, inclusivity, and security. Five case studies across different contexts illustrate both the potential and the gaps in current tools. A sympathetic reader cares because this shifts the goal from faster answers to lasting learner agency and growth.

Core claim

The paper claims that LLMs should be redeveloped as AI learning companions rather than prompted as tutors; these companions integrate a pedagogical foundation on student-AI learning interactions, an adaptive foundation on AI modeling of learners, and a responsible design foundation for transparency and security, with the aim of supporting metacognitive growth, knowledge transfer, and learner agency instead of only immediate performance.

What carries the argument

The three-foundation framework for AI learning companions that combines pedagogy, adaptation to the learner, and responsible design principles.

If this is right

  • AI companions can promote metacognitive development and learner agency by design rather than by accident.
  • These systems adapt to individual students instead of offering uniform task assistance.
  • Responsible design keeps educational AI transparent, accountable, inclusive, and secure.
  • Current tools show partial promise but reveal limitations that the framework can address.
  • Integration across varied educational levels and contexts supports durable understanding over quick outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adoption of the framework might require new evaluation metrics that track transfer and agency instead of task speed.
  • Teachers could gain roles as guides who monitor how companions interact with students rather than direct instructors.
  • The approach opens questions about how to balance AI support with opportunities for productive struggle in learning.

Load-bearing premise

That embedding the three design foundations into LLM agents will actually resolve the paradox and produce measurable gains in cognitive growth and knowledge transfer in real settings.

What would settle it

A randomized classroom study that measures long-term knowledge retention and metacognitive skills after using standard LLMs versus companions built on the proposed framework and finds no advantage or a disadvantage for the companions.

Figures

Figures reproduced from arXiv: 2605.04816 by Dragan Gasevic, Hassan Khosravi, Jason Lodge, Jason Tangen, Kristen DiCerbo, Lixiang Yan, Paul Denny, Ryan S. Baker, Shazia Sadiq, Simon Buckingham Shum.

Figure 2
Figure 2. Figure 2: Framework for building AI companions to support learning. The left cycle represents how view at source ↗
Figure 3
Figure 3. Figure 3: Examples of Khanmigo operating in different interaction modes. Examples of Khanmigo operating in different view at source ↗
Figure 4
Figure 4. Figure 4: Illustrative overview of the RiPPLE platform and its AI-assisted learning workflows. Panel (a) presents view at source ↗
Figure 5
Figure 5. Figure 5: Illustrative examples from the CodeHelp platform. Panel (a) illustrates a guardrailed response in the ‘Code view at source ↗
Figure 6
Figure 6. Figure 6: Overview of the JeepyTA workflow 6.4.2 Pedagogical Foundations of JeepyTA Deep and Interactive Learning. JeepyTA engages students through sustained dialogue. When responding to reflections on readings and lectures, it acknowledges contributions, reinforces key ideas, and connects insights to course themes. It clarifies concepts by summarizing arguments and citing specific readings. When students introduce … view at source ↗
read the original abstract

Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their growing use in education has exposed a learning-performance paradox: while they can enhance short-term task performance, they may also undermine genuine learning, including cognitive growth, knowledge transfer, and metacognitive development. This paper addresses the question of how artificial intelligence should be designed and used to support learning rather than merely improve immediate outputs. We introduce the concept of AI learning companions, defined as adaptive, pedagogically informed, LLM-powered agents designed for integration into learning environments. We propose a framework for their design built on three interrelated foundations: a pedagogical foundation focused on how students learn with AI, an adaptive foundation focused on how AI learns about students, and a responsible design foundation ensuring systems remain transparent, accountable, inclusive, and secure. The framework is illustrated through five case studies spanning diverse educational contexts, levels, and tool designs, revealing both the promise and current limitations of existing tools. We conclude that there is a necessary shift away from LLMs designed for task-oriented performance, and beyond simply prompting them to act as tutors, toward deliberately developed AI learning companions that are pedagogically sound, adapt to their learners, and foster durable understanding, metacognitive growth, and learner agency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper identifies a learning-performance paradox in LLM use for education, where short-term task performance improves but genuine learning, cognitive growth, knowledge transfer, and metacognition may be undermined. It introduces AI learning companions as adaptive, pedagogically informed LLM-powered agents and proposes a framework built on three foundations: pedagogical (focused on how students learn with AI), adaptive (focused on how AI learns about students), and responsible design (ensuring transparency, accountability, inclusivity, and security). The framework is illustrated through five case studies spanning diverse educational contexts, levels, and tool designs, leading to the conclusion that a necessary shift is required away from task-oriented LLMs toward deliberately developed companions that foster durable understanding, metacognitive growth, and learner agency.

Significance. If the proposed foundations can be operationalized with concrete mechanisms and subjected to empirical testing, the work could have notable significance for HCI and educational technology by providing a structured conceptual lens for designing AI systems that prioritize long-term learning outcomes. It usefully synthesizes pedagogical principles with adaptive AI capabilities and responsible design considerations, potentially informing future tool development and research agendas on AI companions.

major comments (2)
  1. [Case Studies] Case Studies section: The five case studies review existing tools and note limitations but supply no new controlled data, implementation details, or outcome metrics showing that the proposed pedagogical, adaptive, and responsible foundations actually improve knowledge transfer or learner agency over standard LLM use. This leaves the central claim that the framework resolves the learning-performance paradox unexamined and untested.
  2. [Framework] Framework section: The adaptive foundation is outlined at a high level as focusing on how AI learns about students, yet no specific mechanisms (such as student modeling approaches, dynamic feedback loops, or methods for encoding pedagogical principles into LLM behavior) are detailed. This specificity is load-bearing for the claim that the companions will adapt to learners and foster metacognitive growth.
minor comments (3)
  1. [Abstract] The abstract would benefit from briefly indicating the specific educational contexts or levels covered in the five case studies to help readers quickly assess the scope of the illustrations.
  2. Consider adding a short subsection on potential evaluation strategies or metrics for assessing the proposed AI learning companions in future work, to bridge the conceptual framework to practical validation.
  3. Ensure consistent use of terminology when distinguishing AI learning companions from prompted LLM tutors, particularly in the introduction and conclusion.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive feedback on our manuscript. We appreciate the positive assessment of the paper's potential significance for HCI and educational technology. Below, we provide point-by-point responses to the major comments and indicate the revisions we intend to make.

read point-by-point responses

  1. Referee: [Case Studies] Case Studies section: The five case studies review existing tools and note limitations but supply no new controlled data, implementation details, or outcome metrics showing that the proposed pedagogical, adaptive, and responsible foundations actually improve knowledge transfer or learner agency over standard LLM use. This leaves the central claim that the framework resolves the learning-performance paradox unexamined and untested.

    Authors: We acknowledge that the case studies do not include new controlled experiments or quantitative metrics, as the primary contribution of the paper is the proposal of a conceptual framework illustrated by existing tools. The case studies serve to demonstrate the applicability of the three foundations across contexts and to identify gaps in current designs, rather than to empirically validate the framework's superiority. We do not claim that the framework has been tested to resolve the paradox; instead, we argue for the necessity of such a shift and provide a lens for future development. In the revised version, we will clarify this scope in the introduction and conclusion, and add a new subsection on 'Limitations and Future Empirical Directions' to outline how the framework could be tested in subsequent studies. revision: yes

  2. Referee: [Framework] Framework section: The adaptive foundation is outlined at a high level as focusing on how AI learns about students, yet no specific mechanisms (such as student modeling approaches, dynamic feedback loops, or methods for encoding pedagogical principles into LLM behavior) are detailed. This specificity is load-bearing for the claim that the companions will adapt to learners and foster metacognitive growth.

    Authors: The framework is presented at a foundational level to establish guiding principles rather than prescriptive implementations, allowing flexibility for different educational contexts. That said, we agree that additional specificity would strengthen the manuscript. We will revise the adaptive foundation section to include concrete examples of mechanisms, such as the use of Bayesian knowledge tracing or deep knowledge tracing for student modeling, reinforcement learning from human feedback adapted for pedagogical goals, and methods like constitutional AI or supervised fine-tuning to encode pedagogical principles. These additions will illustrate potential pathways without implying that current systems fully realize them. revision: yes

standing simulated objections not resolved
  • The provision of new empirical data or controlled studies demonstrating improved outcomes, since the manuscript is a conceptual proposal and review of existing tools rather than an original empirical investigation.

Circularity Check

0 steps flagged

No circularity: conceptual framework with independent case review

full rationale

The paper advances a position on AI learning companions via three design foundations illustrated by five case studies of existing tools. No equations, fitted parameters, or quantitative predictions appear; claims rest on cited external literature and observed limitations rather than reducing any result to a self-defined input or self-citation chain. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on domain assumptions about how AI affects learning processes and introduces a new conceptual entity without independent falsifiable evidence beyond the proposal itself.

axioms (2)
  • domain assumption Current uses of LLMs in education create a learning-performance paradox that undermines cognitive growth, knowledge transfer, and metacognitive development.
    Invoked in the opening to motivate the need for a new design approach.
  • ad hoc to paper A framework built on pedagogical, adaptive, and responsible foundations can guide the creation of AI systems that prioritize durable learning.
    Core premise of the proposed solution.
invented entities (1)
  • AI learning companions no independent evidence
    purpose: Adaptive, pedagogically informed LLM-powered agents for integration into learning environments.
    New term and concept introduced to distinguish from general-purpose or tutor-prompted LLMs.

pith-pipeline@v0.9.0 · 5800 in / 1525 out tokens · 100201 ms · 2026-05-19T17:34:22.625051+00:00 · methodology

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Reference graph

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