Mynard and Ambinintsoa introduce the September 2026 SiSAL Journal issue, weaving nine papers and two book reviews into a four-theme narrative on self-access learning

The introduction organizes the issue's nine papers into a coherent narrative rather than a simple listing, moving through self-access connections with wider learning spaces and communities, the lives and well-being of people within self-access communities, self-regulation and learning processes, and generative AI in self-access language learning. Unlike typical editorial introductions that order papers by submission or loosely by topic, this one states the papers were "organized them in a way that tells a story," giving readers a framework for the whole issue. Based on the introduction's own grouping of four themes and its paper-by-paper descriptions; this is an editorial account of the issue's arrangement.

The introduction identifies the final three papers as jointly highlighting the growing role of generative AI technologies in self-access language learning, with Ubukata and Marzin using generative AI tools to create visual metaphors supporting learner reflection, and open-ended questionnaire responses indicating AI-generated visual metaphors could be a useful resource for promoting reflection. It elevates generative AI from scattered tool experiments to a distinct, grouped theme within the issue, reflecting an emerging research direction in the field. Based on the introduction's summary of the seventh paper; the evidence comes from open-ended questionnaire responses, and the introduction reports no sample size or effect size.

The introduction reports that Andersson's survey of 172 students, containing both quantitative and qualitative measures, found learners viewed AI for independent learning and self-access services as valuable for different purposes and as complementary rather than competing resources for English learning. By surveying 172 students and comparing AI tools with self-access services, it offers learner-perspective empirical evidence for the debate over whether AI replaces self-access centers. The introduction explicitly states a sample of 172 students and notes the survey included both quantitative and qualitative measures.

The introduction also describes Pemberton et al.'s mixed-methods study of six learning advisors using the EmotioNote reflective digital application tool, and Nakanishi et al.'s mixed-methods study of mechanisms supporting persistence in autonomous shadowing during an academic break, which suggests sustained autonomous learning is influenced by adaptive regulation. Placing advisor well-being alongside learner persistence mechanisms in the same issue broadens self-access research's attention to the people involved. Based on the introduction's summaries; Pemberton et al.'s study involved six learning advisors at a Japanese university, and Nakanishi et al.'s was a mixed-methods study.

This introduction is aimed at researchers and practitioners interested in self-access language learning, particularly language-center managers, learning advisors, and language teachers, to quickly grasp the thematic distribution of the September 2026 issue and the positioning of each paper. It suits selection scenarios where readers decide which papers merit deeper reading, and it can serve institutions considering integrating generative AI into self-access services as a directional reference.

The text read here is an incomplete version: the introduction's description of the sixth paper (Anggoro, Pratiwi, and Riadil) breaks off at "The authors," and the description of Al Ghaithi and Behforouz's paper lists only authors and affiliations without research content or results, so the specific findings of these two papers cannot be confirmed from the current text. In addition, the introduction's summaries are overviews that provide no effect sizes, statistical tests, or full sample information, so readers needing to assess evidence strength should consult the original papers. Evaluative phrases such as "promising results" reflect the editors' perspective, and the specific data behind them are not presented here.