In this activity, you will need to share a lesson that you have integrated AI tools into your lesson design or assessment strategies on the SLS platform. If you have not used AI tools before, please share a lesson that you have harnessed the affordances of technology in your classroom. Please include your name before you publish the responses.

If you have not used AI tools before, please share a lesson that you have harnessed the affordances of technology in your classroom. Please include your name and school under "Subject" before you publish the responses.

Electrical Systems ShortAnsFA 2025 (base version)

https://vle.learning.moe.edu.sg/class-group/module/view/a07c4672-0a82-4cf3-9752-5969b14b8ece 

Electrical Systems ShortAnsFA 2025 - HP

https://vle.learning.moe.edu.sg/class-group/module/view/108336dc-9890-49cf-bfce-90a660d33519

Quiz P5 Cycles - Water and Changes of State (Evaporation and Condensation)

https://vle.learning.moe.edu.sg/class-group/module/view/ce7b4b26-22e3-4087-998b-c82e8d36751c

Quiz P5 Cycles - Water and Changes of State (Melting and Freezing)

https://vle.learning.moe.edu.sg/class-group/module/view/aab8c9ec-1b7a-4c9c-8905-75bc20b11610

•introduce Artificial Intelligence (AI) and its tools in Student Learning Space (SLS) / external platforms to support Assessment for Learning (AfL);

•allow primary science teachers to capture, analyse, summarise and visualise learning data to provide students with timely and targeted feedback and resources to move their learning forward.

Specifically:

• Short Answer Feedback Assistant (SAFA) was utilised to provide personalised, automated feedback based on structured model responses, supporting self-directed learning and timely formative feedback.

• DAT was employed to surface common student misconceptions in real-time, enabling teachers to deliver targeted instructional interventions within the same lesson.

ClassPoint:

For a Science lesson on Heat, I used ClassPoint to increase interactivity and monitor learning in real time. Students first observed heat transfer using a thermal camera, then constructed scientific models to represent their understanding, similar to Activity 6.2 in the Primary 4 Workbook. A new context was later provided for them to draw another model to apply their understanding.

With ClassPoint, I was able to view student responses live, allowing for timely feedback and immediate clarification of misconceptions during the lesson.

It is a teacher-marked quiz hence after students complete, teacher reviews the responses and leverages the feedback generated. This feedback is then customised to provide targeted comments to each student.

Pros

●Largely able to correctly allocate the marks

●Encouraging feedback

Cons

●Lengthy feedback

●Over-emphasize on keywords

●Teacher have to do another round of AI Feedback’s vetting

●Accepting “reverse” answers

●Inaccuracy in  accepting of answers

In term 2, we transited to the use of getmarked.ai to generate MCQs onto SLS. It is an auto-marked quiz, allowing immediate feedback and the heat map serves as an item analysis, teacher identifies common misconceptions. This allows for targeted intervention.

In the school, we tap on SLS as a one-stop hub to design learning experiences to develop DCs.

We identify key activities at various levels to develop this DCs.

Digital Information Management (P3 to P6):

e.g. P3: research on how to care for mealworms (act 5.1 page 91 - using Google AI feature

Digital Communication, Collaboration and Participation(P5 to P6):

e.g. P6: adaptations of living things (act 4.2 Nature's Wardrobe- 2014 syllabus)

Students can receive immediate feedback on their responses, allowing them to identify areas where they need to focus their learning and promote active participation. Using the AI function, the teacher can get consolidated responses of the common answers and give feedback on the common errors or learning gaps.

Starter activity on Man's impact on his environment using the theory of productive failure

Using AI to generate ideas for lesson design which was then adapted and differentiated based on student's learning profile.

1. A short answer feedback assistant to facilitate targeted practice and feedback on PSLE open-ended questions.

2. Quizizz, an interactive platform utilized for revision purposes.

3. A word wall, to support LP students in OE questions.

4. ClassPoint to enhance classroom engagement.

5. Pairchat to generate MCQ questions.

6. Padlet as a virtual gallery for students to share their learning.

This enables timely feedback, fosters student engagement, and addresses diverse learning needs.

I also use SLS synchronously in class to better engage students and capture their learning through tools such as SAFA and DAT.

Padlet is used for students to share ideas and collaborate with each other for learning.

In the beginning, we introduced a pre-requisite check to match the learners to their choice of readiness. Additional resources are added to help lower ability learners revise the knowledge. The answers are then assessed by SAFA. This reduces wait time and motivates self-directed learning.

The gamified lesson was introduced with the inclusion of Game Teams. Working as a team to earn XP and moving up the leadership board drive learning. Students who have demonstrated resilience, helped their friends can also be awarded XP.

Please include your group number under "Subject" before you publish your post. Thank you.

Grow: Inaccuracy in marking may seed misconceptions. Teacher will need to review and customise before releasing feedback. Marking on paper seems to be more efficient.

Feedback provided to students may not engage those with literacy needs.

ACP can be used as a bite-sized assessment tool (eg two/ three questions per week) for students to recall content learnt after every lesson instead of waiting for the end-of-topic review worksheet especially since we do not see the students everyday.

Risk

Accuracy of feedback by SAFA

• teachers might need to read the feedback from SAFA before releasing the feedback to the students

• students might feel that they are never good enough from the tone of the feedback given by AI

• AI is not able to mark in totality and middle leniency can give different marks for the same answer

However, possible limitation is with weaker students who had poor language ability who may not be able to comprehend the feedback provided