The collaborative intelligence perspective that anchors the study draws on a body of theory in human-computer interaction and learning science which holds that the most productive human-AI partnerships are those in which the human retains epistemic authority while the machine supplies speed, breadth, and fluency. In writing tasks, this means the AI may suggest phrasing, generate outlines, explain grammar, or summarize sources, but the student must decide what counts as an argument, which claims are defensible, and how the text should be organized for a specific audience. When this division of labor works well, the student learns more efficiently because cognitive load is reduced on low-level mechanical tasks and redirected toward higher-order thinking. When it works poorly, the student risks accepting fluent but shallow or inaccurate output, and the writing becomes a patchwork of machine text that the author does not fully understand or endorse. The Indonesian context adds a distinctive layer to this dynamic, because most generative AI systems are trained predominantly on English-language data while Indonesian students often write in English as an additional language.

English-medium academic writing is a requirement in many Indonesian universities, particularly for students preparing journal articles, theses, and international conference papers. For these students, generative AI presents an alluring proposition: a tireless, instant, and free editor that can smooth grammar, suggest academic vocabulary, and restructure sentences. The study situates itself in this reality, recognizing that Indonesian students arrive at AI tools with genuine practical needs. At the same time, universities in Indonesia, as elsewhere, have scrambled to formulate policies on AI use, with some institutions prohibiting it outright and others encouraging disciplined experimentation. The absence of settled policy creates an environment in which students largely regulate their own AI use, guided by peer norms, instructor expectations, and their own judgments about academic integrity. Understanding what students actually do, rather than what policies assume they do, is therefore an essential empirical task, and it is precisely the gap this research sets out to fill.

Methodologically, the study adopts an assessment-oriented design, gathering evidence of how students interact with generative AI during authentic academic writing activities. Rather than relying solely on surveys about attitudes, the researchers examine the texture of the interaction itself: how students prompt the AI, how they evaluate its suggestions, when they accept, reject, or modify output, and how the final written products reflect the negotiation between human intent and machine generation. This interactional focus is what distinguishes a collaborative intelligence analysis from a simple usage study. The unit of analysis is not the tool and not the student in isolation, but the dyad, the evolving partnership through which a text comes into being. Assessment in this framework is not merely grading the final essay; it is diagnosing the quality of the collaboration, including the student’s critical engagement, revision behavior, and willingness to exercise authorship over machine-generated material.

The findings, as framed by the researchers, point toward a picture of students as active negotiators rather than passive consumers of AI text. Students in the study engaged with generative AI for a range of sub-tasks within writing, from brainstorming topics and generating thesis statements to checking grammar and rephrasing awkward passages. Crucially, the collaborative intelligence lens reveals that the value of these interactions depends heavily on the student’s own disciplinary knowledge and language confidence. Students with stronger command of their subject and of academic English were better positioned to interrogate AI output, spot errors, and reshape suggestions to fit their arguments. Students with weaker foundations were more likely to accept output uncritically, which the study treats as a pedagogical warning sign. This pattern reinforces a conclusion that is emerging across the international research literature: AI literacy cannot be separated from domain literacy, and tools that promise to compensate for weak knowledge may instead entrench it if critical evaluation is absent.

One of the most technically interesting dimensions of the study is its attention to prompting behavior, the set of instructions students type into generative AI systems to elicit output. Prompting is, in effect, a new form of academic skill, blending elements of information retrieval, task specification, and iterative dialogue. Effective prompters provide context, specify audience and register, request alternatives, and feed back corrections across multiple turns. Novice prompters issue vague commands and receive generic text. The study’s interactional data show that Indonesian students vary widely along this spectrum, and that prompting quality is itself learnable. This observation has direct implications for curriculum design. If prompting, evaluating, and integrating AI output are teachable skills, then universities can respond to generative AI not with bans but with instruction, embedding AI interaction training inside existing writing courses where students can practice under guidance and receive feedback on both their prose and their process.

The collaborative intelligence perspective also reframes the perennial anxiety about academic integrity. From this viewpoint, the central question is not whether a student used AI but whether the student can account for the text, defend its claims, and explain its reasoning. A student who uses AI to polish grammar while retaining full ownership of ideas and structure is engaged in a fundamentally different activity from one who submits generated text on a topic they have never researched. The study argues that assessment practices should be designed to make this distinction visible, through oral defenses, process portfolios, draft histories, and in-class writing components that reveal the student’s actual competence. Such approaches align assessment with the reality of professional writing in the AI era, where working academics and professionals already use these tools under norms of disclosure and accountability.

The broader significance of the research extends beyond Indonesia. The country hosts one of the largest higher education systems in Southeast Asia, with tens of millions of students, and its experience with generative AI in writing offers lessons for any education system serving multilingual learners. Generative AI models perform unevenly across languages and cultural contexts, and students writing in an additional language face both opportunities and hazards: the tool can bridge a linguistic gap, but it can also flatten the author’s voice, introduce subtle factual errors, and produce text that sounds native while lacking depth. The Indonesian case thus functions as a natural laboratory for studying how AI mediation reshapes academic literacy in the Global South, where adoption has been rapid but institutional guidance has lagged. The study’s insistence on empirical, interaction-level evidence provides a model for researchers in other national contexts who want to move beyond attitude surveys and policy commentary.

For educators, the practical takeaways are concrete. Writing instruction should explicitly teach critical evaluation of AI output, including fact-checking generated claims against real sources, recognizing hallucinated citations, and revising machine suggestions for argumentative fit. Assessment should reward visible process, not just polished product. Institutional policy should define acceptable use with clarity and nuance rather than blunt prohibition, which research consistently shows drives use underground without improving judgment. And instructors themselves need professional development, because students’ AI practices will only be as sound as the feedback culture surrounding them. The study concludes that generative AI is not an external threat to academic writing but a new participant in it, and that the task of universities is to cultivate collaborative intelligence: partnerships in which human writers remain the authors of their ideas while machines serve as capable, critically supervised assistants.

Subject of Research: Indonesian university students' interaction with generative AI in academic writing from a collaborative intelligence perspective.

Article Title: Assessing Indonesian university students’ interaction with generative AI in academic writing activities: a collaborative intelligence perspective

Article References: Mulyono, H., Suryoputro, G., Azizah, U. S. A., & Falah, Z. A. (2026). Assessing Indonesian university students’ interaction with generative AI in academic writing activities: a collaborative intelligence perspective. SN Social Sciences, 6(9), Article 425. https://doi.org/10.1007/s43545-026-01716-x

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01716-x

Keywords: generative AI, academic writing, collaborative intelligence, Indonesian university students, human-AI interaction, higher education, AI literacy, assessment, prompting, academic integrity, SN Social Sciences, Assessing