Research Highlights | From passive consumers to critical creators: enhancing university students’ new media literacy through AI-enhanced digital multi-modal composing
Editor’s Note
Adhering to the motto “Connecting China and the World through Language, Expressing Global Visions through Words” and fulfilling the educational mission of “Cultivating the Soul of China’s Development, Nurturing Core Talents for International Engagement,” the School of Foreign Languages at Shenzhen University is rooted in the Guangdong-Hong Kong-Macao Greater Bay Area. Guided by the construction of New Liberal Arts, the School deepens the foundation of foreign language and literature while expanding interdisciplinary perspectives. Through diverse integration pathways such as “Foreign Languages + International Communication” and “Foreign Languages + Artificial Intelligence,” it cultivates composite talents with cross-cultural communication skills and innovative thinking, actively serving major national strategic needs.
Guided by the philosophy of “Emphasizing both teaching and research, strengthening the school through research,” the School has launched the “Research Highlights” column to showcase the representative research achievements of both senior professors and young scholars. It aims to build a platform for academic exchange and intellectual dialogue, highlighting the solid research strength and innovative vitality of Shenzhen University’s School of Foreign Languages, and supporting the connotative development of the discipline.
Author Biographies
Zhang Danyang, Associate Dean of the School of Foreign Languages, Shenzhen University; Associate Professor; Distinguished Research Fellow; Doctoral Supervisor; Shenzhen Overseas High-Caliber Talent. She holds a Ph.D. in Education from the University of Cambridge and dual Master’s degrees in Applied Linguistics and Educational Technology from University College London. She has successively led various vertical research projects at national, provincial, and municipal levels, including the National Social Science Fund of China, the Guangdong Provincial Philosophy and Social Science Planning Project, the Guangdong Provincial Education Science Planning Project (Higher Education Special), the Guangdong Provincial Department of Education Graduate Education Innovation Program, the Guangdong Provincial Undergraduate University Teaching Quality and Teaching Reform Project, and the Shenzhen Education Science “14th Five-Year Plan” Key Project. She has published numerous high-level papers in prestigious SSCI and CSSCI journals such as *System, Applied Linguistics, Interactive Learning Environments, Computer Assisted Language Learning, Language Teaching Research, Innovation in Language Learning and Teaching, European Journal of Education,and Technology Enhanced Foreign Language Education. She currently serves as a council member of the China Association of Computer-Assisted Language Learning (ChinaCALL) under the China English Language Education Association (CELEA), a council member of the Foreign Language Educational Technology Professional Committee under CELEA, and a council member of the Pacific Rim Computer-Assisted Language Learning Association (PacCALL). She has served as an academic advisor for the Cambridge China Education Forum and as a sub-forum vice-chair for the Global Chinese Conference on Computers in Education (GCCCE). She has been honored with titles such as “Shenzhen University Youth Pioneer Medal,” “Shenzhen University Outstanding New Supervisor,” “Shenzhen University Excellent Undergraduate Teacher,” and “Shenzhen University Excellent Internship Instructor.” As a key participant, she received the “Second Prize of Guangdong Provincial Higher Education Teaching Achievement Award 2025,” the “Special Prize of Shenzhen Fifth Teaching Achievement Award (Higher Education Category) 2024,” and the “Special Prize of Yantian District First Teaching Achievement Award (Basic Education Category) 2023.”
Main Research Areas: Foreign Language Educational Technology, International Communication, Language Education.
Li Danling, Assistant Professor at the School of Foreign Languages, Shenzhen University; Master’s Supervisor; Shenzhen Overseas High-Caliber Talent. She holds a Ph.D. in Education from the University of Hong Kong. She has led and participated in multiple provincial and municipal education research projects, achieving fruitful results. In recent years, she has published over twenty academic papers in SSCI-indexed journals such as Computers & Education, International Journal of Educational Technology in Higher Education, British Journal of Educational Technology, Computer Assisted Language Learning, Interactive Learning Environments, Innovation in Language Learning and Teaching, Language Learning & Technology, System, RELC Journal, Studies in Higher Education, Higher Education Research & Development, and Research Evaluation. She currently serves on the Editorial Board of Higher Education Quarterly (Early Career Researcher Board). Her research achievements have received the First Prize of the Guangdong Provincial Higher Education Research Outstanding Achievement Award.
Main Research Areas: AI-Assisted Language Teaching, Academic Writing, Higher Education Policy, and Academic Career Development.
Recently, the collaborative research conducted by our faculty members Zhang Danyang, Li Danling, and Professor Guo Kai from The Chinese University of Hong Kong, From passive consumers to critical creators: enhancing university students’ new media literacy through AI-enhanced digital multi-modal composing, was officially published in the SSCI Q1 journal Interactive Learning Environments. This study focuses on AI technology empowering new media literacy education. Through an 18-week teaching experiment, it deeply explores the enhancement mechanism of AI-enhanced digital multi-modal composing on university students’ new media literacy, providing a novel theoretical perspective and practical pathway for media education in the digital age.
1. Research Background
In the current context of increasingly multi-modal and screen-based digital communication, new media literacy has shifted from the “passive consumption” emphasized by traditional media literacy to “active critical creation,” requiring users to possess comprehensive abilities to decode, evaluate diverse media content, and engage in critical production. However, current new media literacy instruction remains constrained by traditional methods. Static lectures and single-mode assessments struggle to cultivate students’ critical production and multi-modal expression abilities, resulting in a teaching gap that emphasizes “analysis over creation.” Against this backdrop, how to leverage AI technology to overcome technical barriers in digital multi-modal composing and build a capability bridge from passive consumption to active creation has become an urgent educational issue.
2. Research Design
This study was conducted within an 18-week course “Journalistic English Reading and Viewing” at Shenzhen University. It selected 41 undergraduate English majors (CEFR B2–C1 level) as participants and employed the AI multi-modal composing platform “Youyan” for the teaching experiment. Students completed a video creation project on AI virtual human-integrated news reporting, including key stages such as video scheme design and presentation, final video production and reporting, personal reflective journal writing, and teacher and peer feedback.
Adopting a mixed-methods approach, the study systematically tracked students’ developmental changes across seven dimensions of new media literacy (access skills, comprehension, analysis, evaluation, creative use skills, dissemination, and creation) through multi-source data: pre/post-course new media literacy questionnaires, student reflective journals, multi-modal news video works, and teacher/peer feedback. It also deeply analyzed the advancement mechanisms of new media competencies during the AI-assisted composing process.
3. Research Findings
(1) Comprehensive improvement across all seven dimensions, with the most significant gain in the creation dimension
Quantitative data showed that students made statistically significant progress across all seven dimensions of new media literacy. Among these, the “creation” dimension demonstrated the most notable improvement (Cohen’s d = 0.79), followed by “comprehension” (d = 0.65), “analysis” (d= 0.64), “creative use skills” (d = 0.57), and “evaluation” (d = 0.50). This indicates that the AI-enhanced digital multi-modal composing model effectively addresses the weakness in fostering creative abilities in traditional teaching, pushing students from simple information integration toward critical creation with ideological reflection.
(2) Progressive advancement in multi-modal abilities: a closed-loop development of analysis-evaluation-creation
Qualitative data revealed the dynamic development pathway of students’ new media literacy:
1.Analysis ability: Students progressed from merely interpreting content to identifying underlying values and biases in media. They became capable of accurately analyzing the limitations of information sources, the emotional tendencies of vocabulary, and ideological positioning.
2. Evaluation ability: They developed systematic judgment across four dimensions of information – timeliness, credibility, objectivity, and social impact – thereby enhancing the quality of their works through diverse source integration, neutral language use, and culturally sensitive design.
3. Creation ability: They achieved narrative restructuring (e.g., shifting from a nationalist framework to a global perspective), structural innovation (e.g., temporal-spatial adjustments, interweaving multi-dimensional narratives), and multi-modal integration (data visualization, audio-video synergy, symbolic design of virtual humans). Their works demonstrated both depth of thought and communicative power.
(3) AI empowerment mechanisms: the dual value of reducing technical burden and focusing thinking
By automating video editing, speech synthesis, and virtual human customization, the AI platform effectively lowered the technical threshold of multi-modal composing, allowing students to reallocate cognitive resources from mechanical operations to higher-order thinking activities. Simultaneously, through human-AI collaboration, students developed technological and AI discernment skills. They critically evaluated the applicability of AI-generated content, supplemented with authentic materials, and optimized AI outputs to ensure the credibility of their works, thus transitioning from “technology users” to “intelligent collaborators.”
4. Pedagogical Implications
Based on the research findings, the team proposed core pedagogical implications for cultivating new media literacy in the AI era:
1. Construct a “analysis-creation” bidirectional integrated teaching model: Break the linear “consume-before-produce” framework. Design iterative composing tasks that integrate analysis throughout the entire creation process, deepening critical thinking through practices such as virtual human design and narrative restructuring.
2. Leverage AI’s scaffolding role: Use AI tools to simplify technical operations, focusing on meaning construction and value expression. Simultaneously, incorporate activities exploring platform limitations and AI ethics discussions to cultivate students’ critical technological awareness.
3.Adopt a multi-dimensional assessment system: Combine quantitative questionnaires, artifact analysis, reflective journals, and teacher/peer feedback to comprehensively evaluate students’ technical application, critical thinking, and creative expression abilities, achieving an assessment closed-loop that values both “process and outcome.”
5. Conclusion
This study confirms that AI-enhanced digital multi-modal composing is not only an effective pathway to improve new media literacy but also reshapes the teaching ecology of the digital age. Through the deep integration of technological empowerment and pedagogical innovation, it successfully transforms students from “passive media consumers” into “active critical creators.” This research outcome provides a replaceable teaching solution for new media literacy education in higher education institutions and offers important theoretical and practical references for the deep integration of artificial intelligence and foreign language education.
Congratulations to the team of Zhang Danyang and Li Danling for this important research achievement!
Writers: Zhang Danyang, Li Danling
