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By using a well-structured Alzheimer’s Disease video as a foundational resource, your interactive design effectively integrates learner-content interaction. The approach of having learners document key facts and then answer specific questions ensures active engagement rather than passive viewing. Additionally, using Kahoot assessments is a great way to reinforce learning and provide interactive feedback.

One area that could further enhance engagement is incorporating peer discussions or collaborative analysis. For example, after answering the questions individually, students could compare their responses in small groups and discuss any discrepancies or surprising insights. This would introduce learner-learner interaction, which can deepen understanding and allow for different perspectives.

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Type of Interaction
​To enhance students’ understanding of how to effectively ask AI questions, I selected a YouTube video explaining strategies for interacting with AI models like ChatGPT. This video introduces essential techniques for structuring clear, specific, and useful AI prompts. Below, I discuss how this video integrates with learning strategies, encourages interaction, and supports inclusive learning.

Respond
The video explores how to frame effective AI queries and introduces strategies to optimize AI responses. While the video itself is not inherently interactive, students are expected to engage in multiple ways, such as taking structured notes on effective vs. ineffective AI prompts, practicing AI questioning by experimenting with different query structures in ChatGPT, and analyzing AI-generated responses to identify patterns of bias, ambiguity, or misinformation. By focusing on learner-generated responses, students actively experiment with AI interactions rather than passively consuming content.

Post-Video Activities for Skill Development
To deepen engagement, I propose several post-video activities. In an AI prompt refinement exercise, students start with a basic AI question and then refine it step by step to improve clarity, specificity, and contextual accuracy. In an AI query comparison task, students analyze and compare AI responses based on different questioning techniques, evaluating which approach yields the most accurate and insightful information. In a real-world AI applications discussion, students identify professional or academic scenarios where effective AI questioning could enhance productivity or problem-solving. These activities ensure that students develop technical proficiency in AI interaction while also fostering critical thinking and precision in digital communication.

Feedback & Assessment Strategy
To ensure effective feedback, students will receive peer feedback by reviewing each other’s AI queries and offering suggestions for improvement. They will also receive instructor feedback on their query refinement process, focusing on logical structuring and specificity. In addition, students will use self-assessment rubrics to evaluate their own questioning techniques and AI response quality. By incorporating multiple feedback sources, students can iteratively improve their ability to engage with AI tools effectively.

Managing Workload & Scalability
This activity design balances effectiveness and feasibility. It is scalable, as it works for both small seminar groups and larger online courses by using AI-generated responses for peer review. It is manageable, as students self-assess their query refinement progress, reducing instructor workload. Most importantly, it is worthwhile, as it encourages active experimentation with AI, a valuable skill in modern education and professional settings.

Ensuring Inclusivity in AI-Based Learning
To address potential learning barriers, I will provide transcripts and subtitles for students with hearing impairments or non-native English speakers. I will offer multiple engagement modes, including text-based exercises, audio reflections, and video demonstrations, to accommodate different learning styles. Additionally, I will use alternative AI platforms such as Google Bard and Claude to allow students to compare AI outputs across different models.

Reference

Jeff Su. (2023, Aug 1). Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)! Youtube. https://youtu.be/jC4v5AS4RIM?si=jEBfBtGiFbYAIDZc

Your combination of multiple teaching methods is excellent. Centering on inquiry-based learning while incorporating other methods can provide students with diverse learning pathways, helping them find the approach that suits them best. The strategy for the presentation segment is also worth adopting. Public speaking is indeed a challenge for many students, and their ability to express themselves should be taken into account to ensure that they can effectively share their learning experiences.

Post #2 – Experiential Learning – Mansahaj

I strongly agree with the experiential learning characteristics you outlined. The field of software engineering indeed requires highly specialized skills, which are built upon continuous experience accumulation. It can be considered one of the fields most suitable for experiential learning. I believe that a quantifiable assessment mechanism is necessary, as it can provide educators with instructional guidelines to ensure teaching quality. However, in the software engineering industry, top talents remain a minority, and quantifiable teaching is still part of foundational education, making it difficult to cultivate elite professionals. Your blog is an excellent experience sharing.

I chose to study experiential learning because all learning processes ultimately need to be tested through practical application. Mastery of many skills is not merely reflected in theoretical knowledge or exam results but requires hands-on practice and real-life experiences. Therefore, experiential learning is one of the most effective learning approaches.

Experiential learning is a method of acquiring knowledge and skills through practical experience, real-life engagement, and self-reflection, emphasizing “Learning by Doing.” Learners engage in authentic or simulated environments, such as internships, field studies, and experimental simulations, and refine their understanding through reflection and application. Yardley et al. (2012) stated that the core of experiential learning lies in practice within real-world contexts, combined with socio-cultural learning theories, allowing learners to construct knowledge through social interaction and experience. This approach is particularly suitable for vocational training, practice-based learning (e.g., legal simulations, social service projects), and courses aimed at enhancing learning motivation. It will serve as a crucial objective and core principle in learning design. Although experiential learning effectively enhances learners’ practical skills and deep comprehension, its implementation requires substantial time and resources, making standardization of learning outcomes challenging. Schrimpf Davis et al. (2021) studied an experiential learning program based on real-world interviews and found that while students benefited significantly, the program faced constraints related to time, funding, and individual differences. Furthermore, Case et al. (2025) pointed out that although experiential learning offers valuable practical opportunities, the absence of structured feedback and assessment mechanisms may impact its effectiveness. Therefore, when designing experiential learning experiences, it is essential to balance real-world engagement with resource allocation while incorporating effective assessment methods.

Experiential learning aligns well with my chosen topic, as the ultimate goal is to develop proficiency and independence in using AI tools. This method fosters learner autonomy, encouraging continuous experimentation until the optimal solution is found. Throughout this process, learners not only enhance their logical thinking but also gain interdisciplinary knowledge by refining their problem-solving approaches. Furthermore, experiential learning is closely related to classroom activities and assignments, as accumulated experience contributes to better questioning logic. This approach also promotes active exploration and peer discussions, ensuring maximum learning effectiveness.

Experiential learning is highly applicable to the final blueprint and interactive learning resources as it emphasizes hands-on practice and active exploration, which enhance learner engagement and comprehension. The team can incorporate assignments, lab work, participation (discussion), and presentations to assess the feasibility of this approach and integrate complementary learning strategies to optimize course design and improve learning outcomes.

References

Case, L., Yun, J., MacDonald, M., Hatfield, B. E., & Logan, S. W. (2025). Assessing the Use of Recommended Practices in Adapted Physical Activity/Education Experiential-Learning Programs. Adapted Physical Activity Quarterly, 42(1), 87–113. https://doi.org/10.1123/apaq.2023-0115

Schrimpf Davis, S., Regan, S., Goodnow, K., Gausvik, C., Pallerla, H., & Schlaudecker, J. D. (2021). Tell Me Your Story: Experiential learning using in‐home interviews of healthy older adults. Journal of the American Geriatrics Society (JAGS), 69(12), 3608–3616. https://doi.org/10.1111/jgs.17483

Yardley, S., Teunissen, P. W., & Dornan, T. (2012). Experiential learning: Transforming theory into practice. Medical Teacher, 34(2), 161–164. https://doi.org/10.3109/0142159X.2012.643264

Could you provide some of your personal experiences to support your views on different teaching strategies? You could delve deeper into one specific teaching strategy. The defined sections in the blog content are quite extensive, and they can actually be supplemented with personal experiences.