Abstract:

This Teaching Development Grant project will advance pedagogical innovation in the faculty by integrating microlearning and generative artificial intelligence (GenAI) into language instruction and language teacher instruction. Microlearning has recently emerged as a learner-centred, research-informed approach to English language teaching, offering targeted content in short, focused bursts (Khong & Kabilan, 2022). In this project, microlearning refers to brief (2–10 minutes), single-outcome units that isolate one subskill, sequence tasks from simple to complex, and limit on-screen elements to what is essential through interactive digital modalities (Kohnke, 2023; Kohnke, 2024). Drawing on cognitive load theory (Sweller, 1994), microlearning reduces students’ cognitive overload primarily by segmenting and goal-specific sequencing. We explicitly minimise extraneous load by avoiding split-attention layouts and redundant text–audio combinations; when we pair modalities (e.g., a concise graphic plus minimal text), it serves germane load (schema building), not additional stimulation. This helps students/teachers to learn more efficiently and supports independent learning. These enhance learner autonomy, support metacognitive development, and align with students’ digital engagement habits (Khong & Kabilan, 2022; Alias & Razak, 2025).

The project will include the design and implementation of GenAI-supported microlearning modules that promote language development, digital literacy, and digital citizenship. These modules will be accessible, ethical, and adaptable, supporting personalised and just-in-time learning for both students and teachers. In our design, these are explicit requirements of each micro-unit (not generic assumptions about all microlearning): accessibility by default (concise layout, alt text, captions), ethical GenAI use (disclosure prompts and permissible-use guidance), and adaptability/personalisation (brief option paths and ‘more detail’ toggles) to match prior knowledge and goals. The integration of GenAI enhances microlearning by enabling real-time content adaptation, personalising feedback, and supporting learner and teacher agency (Szabó & Szoke, 2024). Each unit follows CLT-aligned patterns (worked example → guided practice → brief independent attempt) with optional ‘more detail’ toggles to prevent unnecessary processing. When it is strategically designed and implemented, GenAI-assisted microlearning can foster independent learning while equipping educators with tools for curriculum design, assessment, and multimodal content creation (Kohnke et al., 2025). This aligns with findings that microlearning, when deliberately scaffolded, can offer a structured, low-cognitive-load pathway and strengthen autonomy/SRL through short, incremental tasks (Kohnke, Zou, & Xie, 2025).

While scholars have begun exploring the use of GenAI in language teacher education (Lee et al., 2025), limited research has examined its integration with microlearning or their combined potential for supporting language learners and educators. This project will address that gap by simultaneously developing GenAI-assisted microlearning modules for instructional use and equipping participants with the skills to design their own. This dual approach will not only enhance learners’ linguistic development, digital literacy, and digital citizenship, but will also build educators’ pedagogical capacity through experiential engagement with research-informed frameworks like Technological Pedagogical Content Knowledge (TPACK) and Self-Regulated Learning (SRL).

By uniting learner- and teacher-facing dimensions, the project will offer a scalable, sustainable model of GenAI integration into language education and language teacher training. It also creates clear pathways for future adoption at EdUHK and beyond, reinforcing EdUHK’s reputation for forward-looking, high-quality education. In addition, the project explicitly supports undergraduate IELTS preparation through 2–3 focused Writing modules (Task 1/Task 2) aligned to common EdUHK undergraduate needs. The project includes a sustainability plan to embed modules in core courses, establish maintenance routines, and share open resources for long-term impact. The project includes a clear implementation roadmap and extends existing resources with GenAI-enabled, research-informed microlearning and staff development.

Code:

T0299

Principal Project Supervisors:

• KOHNKE Lucas
• FANGUY II, Michael Paul

Keywords provided by authors:

• GenAI
• microlearning
• language instruction
• teacher professional development
• digital literacy

Start Date:

01 Feb 2026

End Date:

31 Jul 2027

Status:

Ongoing

Result:

1. The development and implementation of a suite of GenAI-supported microlearning modules for language development, digital literacy, and digital citizenship.


2. Capacity-building among pre- and in-service language teachers, equipping them with the skills and confidence (as measured by surveys and participation) to design and deliver their own AI-enhanced microlearning activities.


3. Enhanced student engagement and achievement in English language, as well as in digital and ethical competencies (as measured by surveys, assessments, and analytics).


4. The establishment of scalable models and resources for ongoing staff development and student support in GenAI pedagogy.

Impact:

1. Empowering language teachers to integrate GenAI tools effectively, fostering innovation and adaptability in language education. Indicator: Evidence of continued use the following year (e.g., inclusion of GenAI activities in course outlines and teacher reflections).


2. Promoting a culture of ethical AI use, digital citizenship, and SRL among students and teachers. Indicator: Consistent disclosure and guideline adherence in coursework; recurring ethics/SRL prompts embedded in courses.


3. Raising the standard of language teaching and teacher professional development in Hong Kong’s higher education sector and beyond. Indicator: Requests from additional programmes or partners to adopt/adapt resources; invitations to share practice.


4. Contributing to the existing body of knowledge on AI in education through the sharing of resources, best practices, and research findings. Indicator: Public access/usage of the e-teachers resources and at least one dissemination output referencing project materials.

Financial Year:

2025-26

Type:

TDG