← CHI 2026 map

CHI '26 · Honorable mention · full-paper review · confidence medium-high

Good Fences Make Good Learning: How Self-Directed Language Learners Navigate LLM Delegation Decisions

Jiwon Song , Aeri Cho , Sihyeon Lee , Kiroong Choe , Jinwook Seo

DOI PDF Program page Page JSON

This is a solid CHI paper because it does not just ask whether LLMs help language learning; it asks how learners decide what to hand over and what to keep. The contribution is a useful interpretive framework, but it remains a qualitative account with bounded generalizability.

Axes Lens

Rare contribution shape, typical evidence profile. The point here is not a score. It is to show what kind of claim the paper makes, and whether the evidence pattern is unusual or baseline in this 268 -review set.

Review Summary

This paper’s strongest value is conceptual rather than technical: it reframes LLM use in self-directed language learning as a negotiation over agency, values, and boundaries. Instead of treating delegation as a simple optimization problem, the authors identify three considerations—accuracy, independence, and authenticity—and then organize the decision process around selection and execution obstacles. That is a meaningful CHI contribution because it gives designers and researchers a vocabulary for understanding why learners accept, resist, or partially delegate LLM support. The evidence base is appropriate for that kind of claim: the paper combines analysis of public subreddit discussions with a technology probe involving 13 learners across seven languages. That mixed-methods setup supports an interpretive framework, but it does not justify strong causal claims or broad population-level generalization. The authors are also appropriately cautious about limitations: they note that the SDL-oriented design may invite post-hoc rationalization, that real-life use may involve more spontaneous decisions, and that the participant pool is narrow in age and institutional context. The missing speech/multimodality support further narrows the scope. Overall, I would read this as a well-grounded, thoughtful framework paper with clear relevance to AI-assisted learning design, especially where preserving learner agency matters.

What Changed

Canon before

Prior CHI work on LLMs in learning has emphasized performance gains, tutoring support, and task assistance; this paper shifts attention to learner agency and the values that govern delegation decisions in self-directed language learning.

Departure from common sense

The paper treats LLM delegation in self-directed language learning as a boundary-setting and value-balancing problem, not merely a utility question about whether LLMs improve proficiency. That framing is grounded in the paper’s own emphasis on deciding what to delegate and how, plus the three considerations of accuracy, independence, and authenticity.

Actual novelty

The paper’s main contribution is an empirically grounded account of three delegation considerations—accuracy, independence, and authenticity—organized through two obstacle types: selection challenges and execution challenges. The novelty is less a new system than a new explanatory framework for how self-directed learners reason about LLM use.

Evidence

Evidence supports a mixed-methods contribution: a public-discussion analysis and a technology probe with 13 learners across seven languages. The paper’s claims are primarily descriptive and interpretive, centered on learner values and delegation obstacles rather than causal effects or performance gains.

Limits

Method limits

The study design is qualitative and interpretive, so the framework is grounded in observed discussions and probe sessions rather than controlled comparison. The authors also note that the SDL-oriented setup may encourage post-hoc rationalization, which limits how directly the findings capture spontaneous real-world delegation behavior.

Deployment limits

The findings are most applicable to self-directed language learners who actively negotiate when to use LLMs. The paper’s design implications are about preserving agency in AI-assisted learning systems, but the evidence does not establish effectiveness across broader educational settings or learner populations.

Boundary conditions

The paper’s own limits suggest caution for learners outside the sampled context, especially because participants were aged 20–30 and drawn from a Korean university. The probe also lacked a speech module and did not capture multimodality, so the framework may not transfer cleanly to spoken or multimodal language-learning workflows.

Manual check: yes

Position in field

This paper sits at the intersection of HCI, AI-assisted learning, and self-directed language learning. Its contribution is to reframe LLM support as a delegation-governance problem, complementing prior work that focused more on proficiency outcomes or generic assistance.

Abstract

Self-directed language learners increasingly turn to large language models (LLMs) for assistance, but face the challenge of deciding what learning tasks to delegate to LLMs and how. While prior research has examined the effectiveness of LLM in improving language proficiency, less is known about how learners negotiate agency and what values guide delegation strategies. To address this gap, we conducted a two-part study: an analysis of discussions in the r/languagelearning subreddit to map learners' LLM usage patterns and factors driving delegation, followed by a technology probe study where learners designed learning activities and experimented with LLM support. Our findings reveal three key considerations influencing delegation: accuracy, independence, and authenticity. We analyze these considerations through two types of obstacles: selection challenges in choosing appropriate strategies and execution challenges in following through on intentions. These insights inform the design of AI-assisted learning systems that preserve learner agency while supporting diverse learning goals.