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📄 Research Article

Students' Perceptions of Multiliteracies Development Using AI-Assisted Portfolio Assessment

◐ medium · 2026-08-20 · eportfolio, portfolio-assessment, generative-ai, assessment, feedback, language-learning, student-engagement

Synthesis: Ni and Lam (2026) report a 16-week qualitative study in which 50 first-year Tourism Management students at a Chinese university collaboratively compiled WordPress-based e-portfolios of multimodal writing about touring-route recommendations, supported by Generative AI (GenAI) tools as direct Feedback providers and as moderators/synthesizers of peer and teacher feedback. Across reflective journals, focus-group interviews, and narrative inquiries, students developed multiliteracies through three stages, transforming from passive to proactive learners while gaining multimodal awareness, confidence, metacognition, and professional readiness. Students perceived GenAI as a study companion offering instant, gentle, rubric-referenced Feedback, but also faced challenges including over-trust in AI and information overload. The study positions GenAI-assisted portfolio assessment as a transformative literacy practice within Formative Assessment.

Key Findings

  • Three-stage multiliteracies trajectory: students moved from AI-empowered curation and redesign of multimodal artefacts, to augmented confidence in multimodal meaning-making, toward readiness for real-world multimodal communication — a shift from passive recipients of knowledge to proactive, self-regulated learners.
  • AI-enhanced Student Engagement: the recursive create–curate–reflect–revise cycle, scaffolded by GenAI suggestions on imagery, colour schemes, and layout, deepened engagement and design thinking (e.g. Ella's group adopting low-saturation colours; Student 12 harmonizing a colour palette and adding a Li Bai quotation hook).
  • Expanded usefulness of portfolios: students gained competence in managing digital platforms (WordPress), crafted audience-aware artefacts with a global reach, and connected the project to career demands (CET certificates, video editing, multimodal self-media aesthetics).
  • Critical multisource feedback evaluation: students learned to selectively integrate peer, teacher, and AI feedback rather than adopt it wholesale, using AI to explain or synthesize teacher comments — though many initially treated AI output as authoritative (Lucy: "I'd unconsciously trust it").
  • Metacognitive and confidence gains: gradeless, feedback-rich portfolio compilation (vs. high-stakes exams) reduced writing anxiety, fostered learner agency, and surfaced "aha moments" where self-assessment diverged from teacher/AI feedback (e.g. FG3-S1 revising "red spirit" to "revolutionary spirit" for intercultural clarity).
  • Co-constructed rubrics as catalysts: rubrics jointly built with students guided self- and peer-assessment, feedback provision, and better prompting of AI for rubric-referenced feedback.

Study Design & Method

  • Design: qualitative, interpretivist study over a 16-week semester examining students' multiliteracies development and perceptions of GenAI use in portfolio compilation; guided by two research questions on development and on perceived affordances/challenges.
  • Participants: 50 first-year Tourism Management majors (CEFR A1–B2) with no prior academic AI experience, in an application-oriented Chinese university.
  • Intervention: students worked in 11 groups to create WordPress-based individual expository + argumentative essays synthesized into a PowerPoint group portfolio on touring-route recommendations; AI feedback (e.g. ERNIE-BOT, Kimi) provided immediate feedback on language, organization, and multimodal integration, and facilitated feedback uptake and synthesis across three weekly writing cycles.
  • Data & analysis: 50 reflective journals, 4 focus-group interviews (~1 hour each), 6 narrative inquiries (30–45 min each), plus multimodal analysis of artefacts; iterative inductive thematic analysis conducted in Chinese and translated to English by the author team.

Implications for AI in Education

  • Position AI as a co-partner, not a replacement: students gained most when GenAI supplied scaffolded, dialectical feedback and design consultations while they retained agency over their meaning-making and revision choices — consistent with "cyber-social literacy learning" paradigms.
  • Design gradeless, feedback-rich Formative Assessment: pairing GenAI support with low-stakes, multisource Feedback reduced anxiety and boosted engagement relative to high-stakes examinations, supporting Self Regulated Learning.
  • Scaffold critical AI engagement: without training, students over-relied on AI output; explicit instruction in prompt refinement, critical evaluation of multisource Feedback, and Feedback Literacy is essential to prevent over-trust and information overload.
  • Use co-constructed rubrics and Authentic Assessment: rubrics that extend beyond linguistic accuracy into multimodal orchestration help students prompt AI for relevant feedback and guide self- and peer-assessment.
  • Address equity and ethics: teachers need structured support so that GenAI-assisted Formative Assessment remains equitable and does not displace critical thinking or learner autonomy.

Connected Concepts

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Citation

Ni, Y., & Lam, R. (2026). Students' perceptions of multiliteracies development using AI-assisted portfolio assessment. Pedagogies: An International Journal, 21(2), 301–328.