Examination of a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era shows that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone.
Abstract
Generative artificial intelligence (AI) is increasingly shaping how university students search for information, write, create, communicate, and solve problems. In higher education, this situation requires not only operational skills for using AI tools, but also broader competencies such as critical evaluation, information literacy, ethical judgment, self-regulation, and collaborative reflection. This study examines a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era. The activity was conducted at two Japanese universities. Students received a common instructional presentation on digital safety, misinformation, AI risks and benefits, cognitive bias, and digital well-being. They then discussed the question, “What competencies should university students develop in the AI era?” and created collaborative mind maps using Personary. Student-generated mind maps and written reflections were analyzed through interpretive map analysis and text-mining-assisted qualitative analysis. The results show that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone. Their maps and reflections emphasized critical evaluation of AI-generated information, media and data literacy, autonomous thinking, communication, ethical responsibility, appropriate AI use, and adaptability. Personary supported the externalization and organization of these ideas, while the AI-assisted mode provided additional prompts for expanding selected branches. The study demonstrates how AI-supported mind mapping can function as a reflective learning activity for visualizing, sharing, and reorganizing students’ understanding of AI literacy in higher education.
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
The increasing use of artificial intelligence (AI) in education has raised important questions about how learners and teachers regulate their thinking when interacting with AI-supported tools. This study investigates metacognitive regulation during the design of AI-supported educational applications by graduate students (N = 12) in a UAE teacher education programme. The qualitative research adopted a multiple-case study method to analyse six student design teams involved in designing educational applications as part of a technology integration course. Data were collected from three main sources: the educational apps themselves, the corresponding Teacher Guides, and reflective interviews conducted using a semi-structured interview guide. Data analysis combined deductive qualitative coding based on the regulation-of-cognition dimensions of the Metacognitive Awareness Inventory (MAI) framework with inductive thematic analysis of participants’ experiences and conceptualisations of AI. The findings show that AI was used differently across the six cases. It appeared to function more as a cognitive scaffold when participants questioned, checked, revised, or justified AI-generated outputs, and more as a shortcut when outputs were used mainly for convenience with limited further reflection. Planning was the most visible regulatory process, while stronger regulatory engagement was evident when AI-generated outputs were examined, questioned, and refined rather than accepted without further consideration.
The study highlights the importance of integrating AI into ESP instruction through pedagogically sound practices supported by ethical guidance and digital literacy by providing context-specific insights that can inform the design of responsible, effective, and ethically grounded AI-enhanced ESP instruction.
D. Zulaiha, Yunika Triana· Journal of Educational Manag...· 0 citations
Abstract In the last few decades, data-driven learning and generative artificial intelligence (AI) applications have become increasingly visible in academic writing instruction. The data-driven learning approach promotes students’ lexical and morphological awareness by encouraging them to explore language in context, while generative AI tools offer new possibilities for idea generation, text development, and linguistic feedback. However, limited process-based studies exist on how these two approaches work together in collaborative writing in the classroom and how teacher guidance shapes this process. This study aims to explore how B2-level students enrolled in a university preparatory program use data-driven learning and generative AI tools in the collaborative opinion essay writing process. Designed as a qualitative classroom-based case study, the research was conducted over a seven-week module. Structured student diaries, teacher journals and semi-structured interviews were used in the data collection process. Findings indicate that tool use was constructed through peer negotiation and teacher mediation. Students used generative AI for idea generation and corpus tools for lexical verification and contextual checking. The study highlights the critical role of pedagogical design and teacher facilitation in DDL-AI-assisted writing instruction.
Buse Uzuner· Digital Studies in Language...· 0 citations
This case study examines how students were positioned as experts in shaping artificial intelligence (AI) literacy curricula at a United Kingdom university. Academic staff, students and professional services collaborated to co-create an AI literacy framework with a focus on generative AI, addressing institutional and learner needs. Guided by partnership principles of reciprocity, respect and shared responsibility, the project responded to the opportunities and challenges of AI in higher education (HE).
Following a cross-disciplinary survey which captured student perceptions of generative AI use, two student partners were appointed; their roles evolved from research interns to co-designers and co-evaluators during this funded project. They have co-led focus groups with students, conducted data analysis and synthesis and undertaken peer consultation and evaluation to co-develop guidance resources on ethics, integrity, bias and responsible AI use.
Two key outputs resulted from this collaboration: a four-step AI literacy framework and an online tutorial which are now embedded in central pre-enrolment and academic skills programmes. Framing students as co-designers helped both 1) to enrich staff understanding of student perceptions of generative AI use for learning and 2) to enable the students to enhance their research, collaboration, communication and leadership skills. The study contributes to the current discourse on fostering critical, responsible AI literacies in HE through the lens of students as partners.
Nurun Nahar, David Howard, Kater Akeren et al.· Compass: Journal of Learning...· 0 citations
Generative artificial intelligence (GenAI) can improve efficiency in academic work. However, it also raises a central psychological question: when AI helps produce a task, why do some learners still experience the final work as their own, whereas others experience it as polished but psychologically distant? This study examines psychological ownership and competency anxiety in AI-assisted learning, distinguishing between dependent AI outsourcing and reflective human–AI collaboration. An exploratory qualitative interview-based design with supplementary background information was used. The background form was used only for sample description and interview preparation, followed by in-depth interviews with 50 undergraduate and postgraduate students from five Chinese universities, interviews with 10 faculty members and administrators, and 128 student critical incident records. Data were analyzed through a hybrid deductive–inductive thematic analysis, with NVivo 14 used as a supporting tool for code management and matrix comparison. In participants’ accounts, dependent AI outsourcing was associated with weaker psychological ownership, described in terms of reduced cognitive and authorial presence during planning, reasoning, and revision. This weakened sense of ownership was, in turn, linked to shallow memory, difficulty explaining an individual’s work, reduced meaning-making, and stronger competency anxiety. Reflective human–AI collaboration, by contrast, was associated with retained ownership when students planned before using GenAI, evaluated AI output, revised it in their own voice, and could explain their final decisions. Perceived educational support, including feedback, clear AI-use guidance, and psychologically safe learning environments, was described as helping students treat AI use as a learnable practice rather than a hidden shortcut. Faculty and administrators also framed these patterns as issues of assessment feasibility, workload, policy clarity, and curriculum design, rather than solely as student choice. Because the data were collected at five Chinese universities, the proposed model is presented as context-sensitive: the observed dynamics may be amplified where assessment is strongly product-oriented, formative feedback is constrained, and AI use is difficult to discuss openly. This study contributes to psychological ownership theory by extending it to human–AI interaction and proposing that, in this setting, ownership depends on cognitive and authorial presence rather than solely on task completion.