AI Reasoning
Introduction: A New ERA in AI Reasoning
Artificial intelligence is often thought of as an instantaneous response system—quick, efficient, and direct. But what happens when an AI model takes the time to "think" before responding? That’s precisely what I observed when testing the Deepseek-R1-Distill-Qwen-7b model on my home lab computer. Unlike the typical AI behavior of immediately generating an output, this model demonstrated a structured reasoning process before producing a response. This shift toward deliberate reasoning has profound implications for instructional designers (IDs) who rely on AI-driven tools to develop, assess, and enhance educational experiences.
However, it is crucial to understand that all the detailed improvements I discuss in this article depend heavily on the initial crafting of the prompt and the quality of the prompt itself.
The effectiveness of AI-generated instructional materials is directly tied to the expertise of the individual prompting the model.
A generic prompt, written by someone without a solid understanding of instructional design, would likely yield a lower-quality output that lacks pedagogical depth, alignment with learning theories, and structured cognitive engagement. The AI, while sophisticated, is still dependent on the user's ability to frame a well-defined and meaningful query. This is an essential point for instructional designers to recognize—no AI development or improvement will replace the education, experience, and strategic thinking of human instructional designers. Instead, AI is a tool that extends and enhances an ID’s expertise, not a replacement for it.
In this article, I will break down the unique thinking capabilities of the Deepseek-R1-Distill-Qwen-7b model, its potential benefits for instructional design, and how such advanced reasoning can reshape the future of AI-assisted education.
Observing AI "Thinking" in Action
When I prompted Deepseek-R1-Distill-Qwen-7b with a complex task—developing a rubric to analyze the Robert Hanssen espionage case for post-graduate learners—the model did something remarkable. Instead of immediately generating a rubric, it paused to process the request with structured reasoning. It took approximately 58 seconds to methodically break down the task, organize its approach, and strategize the best way to generate a meaningful output.
This wasn’t a simple regurgitation of facts. Instead, the model outlined its thought process in an almost human-like fashion. First, it worked to understand the broader context of the task, identifying that the rubric needed to cater to post-graduate students in law enforcement, intelligence studies, and forensic psychology. Recognizing that these fields require critical thinking and deep analysis, the model set a foundation that would ensure the rubric aligned with these expectations. Next, it identified and applied relevant instructional design theories. By incorporating Andragogy, Social Constructivism, and Constructivism, the AI ensured the rubric was tailored for adult learners, emphasizing cognitive maturity and knowledge-building through context and personal experience. This alignment with learning theory was critical because it meant that the AI was not just generating a rubric but creating an educational tool that would foster meaningful engagement.
Another fascinating aspect was how the model managed cognitive load. It structured the rubric into digestible sections, carefully segmenting information to prevent overwhelming learners. This aligns directly with Cognitive Load Theory, which stresses the importance of organizing information into manageable chunks to optimize learning efficiency. In addition, the AI incorporated diverse assessment strategies, blending open-ended, multiple-choice, and reflective exercises to encourage active engagement and deep comprehension.
As the final step, the model applied Bloom’s Taxonomy to establish a clear framework for assessment. It categorized learning objectives into knowledge recall, application, analysis, and synthesis, ensuring a comprehensive evaluation of the case study. By reasoning through these steps before outputting the rubric, the AI demonstrated an advanced level of pedagogical awareness, producing a response that was far superior to a simple automated answer.
Why This Matters for Instructional Designers
The ability of AI to think critically and structure responses with educational rigor is a transformative development for instructional design. Traditionally, AI has been seen as a tool that provides rapid answers, but this new reasoning-based approach changes the dynamic entirely. Instead of merely automating content generation, AI can now serve as a genuine thought partner, capable of understanding complex instructional needs and aligning content accordingly.
For instructional designers, this means AI can contribute more meaningfully to course development.
Instead of requiring extensive human refinement, AI-generated materials are now pedagogically sound from the outset.
When designing assessments, for instance, AI can analyze learning objectives and propose targeted evaluation strategies that match Bloom’s Taxonomy. This ensures that students are not only tested on their ability to recall facts but also challenged to apply, analyze, and synthesize knowledge effectively.
Another key advantage is the customization AI can provide in addressing learners' unique needs. The model’s ability to recognize instructional theories means it can personalize learning experiences in ways that were previously unattainable. Adaptive learning paths can be designed based on students’ prior knowledge, ensuring that content is neither too simplistic nor overwhelmingly complex. Similarly, scaffolded feedback can be generated dynamically, helping students refine their understanding step by step rather than receiving generic corrections. By doing so, AI supports differentiated instruction, making learning more inclusive and accessible to a diverse range of students.
The AI’s constructivist approach also mirrors the best practices in instructional design, particularly for higher education and professional training. Rather than presenting static information, it guides learners through a structured analytical process, encouraging them to construct knowledge through case-based learning, collaborative discussions, and reflective analysis. This methodology aligns with how instructional designers build curricula that promote engagement and long-term knowledge retention. The Hanssen case study is an excellent example of this, as it allows learners to engage with real-world intelligence failures, analyze organizational dynamics, and reflect on ethical considerations—all of which contribute to a deeper understanding of the subject matter. As AI continues to evolve, instructional designers will find themselves working alongside increasingly sophisticated models that do more than generate content; they will facilitate cognitive development, reinforce metacognition, and enhance the overall learning experience. This represents a paradigm shift in how AI is integrated into educational settings, moving away from automation and toward intelligent augmentation.
Practical Future Applications of AI in Instructional Design
The future of AI-assisted instructional design is not about overhauling existing learning management systems or requiring deep technical expertise. Instead, it lies in practical, achievable improvements that instructional designers can implement with ease. One major area of impact is better product design. AI can assist in refining instructional materials by ensuring clarity, alignment with learning objectives, and consistency in tone and difficulty level. This makes course development faster and more efficient while maintaining high educational standards.
Another tangible benefit is improved data analysis at the local level. AI can help instructional designers track learner performance trends, pinpoint areas where students struggle and suggest modifications to course materials in real time. Instead of relying on large-scale AI integrations, IDs can use AI-generated insights to make small, incremental improvements that lead to significant gains in learner engagement and comprehension. Additionally, AI can enhance microlearning by helping instructional designers create short, focused learning modules tailored to specific skill gaps. This ensures that learners receive just-in-time training that is both relevant and effective. For IDs working with limited resources, AI’s ability to assist in content repurposing—such as transforming lengthy text-based content into interactive exercises or video scripts—offers a major efficiency boost.
By focusing on these practical applications, instructional designers can harness AI’s growing cognitive capabilities without requiring extensive technical knowledge. Rather than reinventing their workflows, they can integrate AI as a powerful tool that enhances what they already do best: creating engaging, effective, and learner-centered educational experiences.
