AI-Integrated Teaching and Learning Model in Sec 3 HCL

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Department: CLC

Leaders: Yeo Hwee Yanne

Members: Qi Yanping, Sun Heyu, Cao Jinghua, Yang Liu, Ni Qing

1.  What was the current need/gap that you were addressing?

Students lacked depth and confidence in oral and essay writing. Inquiry-based learning cycles often exceeded available curriculum time, resulting in uneven progress across classes. Root causes included the need for sustained practice beyond lesson time, varied levels of digital literacy, and inconsistent metacognitive habits. Some students tended to rely on AI for quick answers rather than deeper inquiry, signalling a need for clearer structures and differentiated support to strengthen both engagement and independent learning.

2.  How had it been experimented and enacted?

Across the year, the Sec 3 HCL team piloted an AI-integrated learning model to extend learning beyond the classroom and enhance engagement. SLS was used as the central platform and supported by complementary ed-tech tools aligned to different KAT outcomes. 1. SLS provided structured, self-paced practice for the ELD thematic news review packages rolled out regularly in Term 2. Its Learning Assistant SALiS feature enabled sequential and personalised learning conversations during the Term 3 asychronous inquiry-based package, while Data Assistant functions supported teachers’ monitoring of student progress and misconceptions. 2. Snorkl enabled extended oral practice with instant AI-generated feedback on fluency, structure, and content, based on the prompts given by the teacher. 3. NotebookLM supported idea organisation, synthesis of information, and deeper thinking for both oral and essay tasks. This combination of ed-tech platforms strengthened meaningful exploration, active participation, and authentic application of language skills amongst the Sec 3 students.

3.  Which group(s) had benefited?

Students (Entire Cohort), Teaching Staff (Selected Groups), Others

4.  What was the positive impact?

[For students] Immediate and meaningful feedback: Snorkl and SLS provided timely, targeted responses that improved oral proficiency, writing content, and conceptual understanding. Stronger autonomy: NotebookLM and SLS supported self-paced learning and encouraged students to take greater ownership of their progress. Student survey data highlighted three strong themes: increased engagement, effective feedback, and greater flexibility for self-directed learning. These qualitative findings validated the model’s impact and guided ongoing refinements across terms. [For teachers] Data-informed support: SALiS analytics provided insight into student learning patterns and enabled differentiated intervention. Professional contribution: Two Sec 3 HCL teacher teams shared this AI-integrated model at the 2025 West Zone CL Teaching and Learning Symposium, promoting wider adoption across schools.

5.  What is a future need that this IdEas@work could meet?

Future work will focus on curating reproducible, differentiated AI-enhanced resource packages that can be scaled across the four-year HCL curriculum. Continued effort will also deepen students’ AI literacy and critical questioning skills, supporting responsible, reflective, and sustained self-directed learning.




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