Stimulating Curiosity
(This reflection captures the most meaningful impacts on my thinking from studying Teaching with Technology – GRAD 704, University of Nevada, Reno, taught by Wenzhen Li, PhD.)
As a software developer, my primary interest for many years has been how technology can genuinely elevate thinking. This course helped clarify how symbolic thinking—at which computers excel—differs from, yet interacts with, the semantic and conceptual thinking that humans employ. Several of these insights have led directly to design changes in two programs I am developing – LinguaFormula and Quality Research Studio.
Questions and Mystery – Why "Cliff-Hangers" Are Effective
It had never dawned on me before that questions encapsulate mystery—and that mystery is what draws us forward. When asked well, questions create engagement and curiosity; they generate a sense of intrigue.
The most significant recent shift in my thinking about education has been a reorientation toward assessment—not as a tool for evaluation, but as a driver of learning. Questions reveal gaps in understanding, awaken curiosity, force retrieval, and open opportunities for feedback and metacognition. Rather than interruptions to learning, they are the mechanism by which openness is evoked.
Practical application: designing around inquiry
This shift has already influenced how I design learning systems. In LinguaFormula, it has led to a stronger emphasis on continuous recall and interaction rather than end-of-lesson testing alone. In Research Studio, it has reinforced the idea that inquiry should be organized around open questions—“mysteries”—rather than static topics. If I were to summarize this change, it would be that learning improves when students experience themselves as active participants in a process of discovery, rather than passive recipients of information.
Another realization: the need for visual representation
A second, closely related realization involves the role of visual representation of ideas in teaching and learning. I have become more aware that my own and other people's understanding is often visual in nature. When we grasp a concept, it often appears first as a kind of internal structure—something relational or spatial—rather than as a sequence of words. This makes it clear that effective teaching cannot rely on text alone; in many cases, concepts must be seen as well as verbalized.
A limitation: translating thought into visual form
At the same time, this recognition has exposed a limitation in my own abilities—and likely in those of many teachers whose verbal skills are more developed than their visual design skills. There are exceptions, of course, but most of us have spent years learning to write and comparatively little time learning to draw.
When understanding is visual, our internal images are often dynamic, abstract, and difficult to translate into standard two- or three-dimensional representations. Even when I have a clear idea of what I want to present, I find graphic design tools difficult to use. Traditional drawing is not a strength of mine, and both technical and artistic design tools tend to involve steep learning curves that slow down experimentation.
Current approach: an experimental phase
My response to this tension—between the importance of visual presentation and the difficulty of producing it—has been exploratory. I have begun using AI-based image generation to build a reusable library of visual elements for lessons—a kind of “visual vocabulary.” I am also exploring ways to integrate image generation directly into the lesson creation process, so that visuals are developed alongside text rather than added afterward.
Direction for growth
Two central principles are clear: learning is driven by inquiry, and understanding is often supported by visual structure. While my current implementations are still developing, these ideas are already shaping the design of Lingua Formula and related systems.
The direction forward is clear. Learning environments should require active thinking through questions, while also providing visual and conceptual structures that make relationships easier to see. Technology, when used well, can support both—helping students not only access information, but engage with it more directly and meaningfully.
Reference Hub
This section serves as a curated, reusable index of key sources on teaching, assessment, inclusion, and AI in education garnered from GRAD 704, University of Nevada, Reno, taught by Wenzhen Li, PhD, in the spring semester of 2026.
Teaching Journal Directories
1. Foundations of teaching and instructional design
- Wiggins & McTighe — Understanding by Design
- Nilson & Goodson — Online Teaching at Its Best
- Nilson — Teaching at Its Best
- Clark & Mayer — E-Learning and the Science of Instruction
- Mayer — Multimedia Learning (2009)
2. Assessment and feedback (core to learning)
- Palomba & Banta — assessment as systematic improvement of learning
- Northern Illinois University — assessing student learning
- Penn State — online assessment
- UNR — best practices for online assessment
- Sewell et al. — online assessment strategies (MERLOT)
- Rhodes — The Value of Learning (AAC&U)
- Sparks — types of assessments (Education Week)
- Pappas — formative assessment in eLearning (eLearning Industry)
- Pappas — summative assessment in eLearning (eLearning Industry)
3. Inclusive teaching and UDL
- CAST — UDL guidelines
- Tobin & Behling — Reach Everyone, Teach Everyone
- Addy et al. — What Inclusive Instructors Do
- Yale Poorvu Center — inclusive teaching strategies
- Cornell — building inclusive classrooms
- Columbia CTL — inclusive teaching guide
4. Practical teaching strategies (highly applied)
- Faculty Focus — engagement in online labs
- Faculty Focus — alternative assessment methods
- Faculty Focus — student-centered teaching via formative assessment
- Talbert — agile student feedback
- Dunham — group project design
- Maloy & Trust — students as OER creators
5. Reflection and student engagement
- Colella — self-reflection assignments
- Huang — implementing learner reflection
- Howard — small group check-ins (SEL)
6. Exams, academic integrity, and online testing
- Ganzert & Shipp — midterm assessment plan
- McLaughlin & Ricevuto — assessment without lockdown browsers
- Kimmons & Veletsianos — proctoring software in higher education
7. AI in education (major emerging area)
Core guides and platforms
- University of Sydney — AI for educators
- University of Sydney — AI in education (student-facing)
- AI misconceptions guide (University of Sydney)
- Cornell — ethical AI in teaching
Books and thought leaders
- Bowen & Watson — Teaching with AI (Teaching Naked)
- Mollick — Co-Intelligence (One Useful Thing)
- Mollick — practical guide to using AI
- Mollick — assigning AI (SSRN)
Research, articles, and policy
- Educause — AI and the future of higher education
- AAC&U — AI challenge report (PDF)
- World Economic Forum — AI and jobs
- Zakrajsek — AI and metacognition (Scholarly Teacher)
- AI assessment scale