AI should make people more capable, not less

Why Usewright exists

AI can now produce most of what a course or a thesis asks for. That makes it easy to finish the work without building the competency the work was meant to develop (the knowledge, skills, and judgment behind it), often without noticing. Rules alone do not solve this, and detectors make it worse by turning learning into suspicion.

Usewright takes a different approach. It treats deciding how to use AI as a competency in its own right, one people can practice, reflect on, and show. Choosing not to use AI for a good reason counts as much as using it well.

Usewright generates nothing for you: no answers, no drafts, no summaries of your work. Its value is measured only by what you can understand, explain, and do afterwards, on your own. That is also why it works with any AI tool, today's or tomorrow's. It is about your judgment, not about a particular product.

Principles

• Judgment, not policing. Usewright never detects AI or monitors anyone.
• Privacy first. Instructors see patterns, never individual entries. People own their records.
• Grounded in learning science. Each step draws on established research on how understanding lasts.
• Open to every discipline. Designed with engineering and STEM in mind, useful anywhere.

The research behind each step

• Explain without AI. People often believe they understand something until they try to explain it (Rozenblit & Keil, 2002), and explaining in your own words deepens understanding (Chi et al., 1994).
• Revisit later. Recalling something after a delay strengthens long-term memory more than rereading it (Roediger & Karpicke, 2006). Students who got reminders came back to reflect significantly more often than students who did not (Butt & Menekse, 2023), so Usewright lets you add each revisit to your own calendar.
• Plan and try first. Unrestricted AI help improved practice scores but lowered later exam performance; guardrails largely prevented the harm (Bastani et al., 2025). After an authentic, hands-on learning activity, fewer undergraduates turned to AI on a conceptual test (Anwar et al., 2026).
• Be specific. Specific reflections, rather than vague ones, are a mark of reflection quality (Anwar et al., 2018), so Usewright asks you to name the step, idea, or decision that makes your work hold up.
• Policy levels. Clear, task-level expectations for AI use follow the AI Assessment Scale (Perkins et al., 2024).
• Building confidence in your own ability. Among knowledge workers, more trust in AI went with less critical thinking, and more self-confidence with more (Lee et al., 2025).

1. Anwar, S., Bahng, D. S., Stepanova, A., Belanger, C., Hammond, T. A., Cherry, K. S., & Ali, S. F. (2026). Impact of the FossilSketch application on students' use of generative artificial intelligence for conceptual assessment [Conference paper]. 2026 ASEE Annual Conference & Exposition, Charlotte, NC, United States. nemo.asee.org
2. Anwar, S., Menekse, M., Stratton, D. H., & Kim, D. (2018). Work in progress: Students' reflection quality and effective team membership [Conference paper]. 2018 ASEE Annual Conference & Exposition, Salt Lake City, UT, United States. peer.asee.org
3. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
4. Butt, A., & Menekse, M. (2023). The impact of reminder nudge on STEM students' application engagement. In INTED2023 Proceedings. IATED. https://doi.org/10.21125/inted.2023.2170
5. Chi, M. T. H., de Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. https://doi.org/10.1016/0364-0213(94)90016-7
6. Freeman, J. (2025). Student generative AI survey 2025. Higher Education Policy Institute. hepi.ac.uk
7. Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3706598.3713778
8. Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
9. Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
10. Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26(5), 521–562. https://doi.org/10.1207/s15516709cog2605_1

Why it matters for engineering

The engineering profession is clear that AI does not replace judgment. ASCE's Policy Statement 573 states that “AI cannot serve as a replacement for the professional judgement of a licensed Professional Engineer,” and the National Society of Professional Engineers calls for continuous education as AI spreads. ABET's student outcomes ask programs to develop the ability to make informed judgments about professional responsibilities (outcome 4), to use engineering judgment to draw conclusions (outcome 6), and to use appropriate learning strategies (outcome 7).

Usewright practices exactly these habits on everyday coursework: deciding when a tool fits the task, checking its output, owning the reasoning, and explaining it without help.

1. ABET. (2025). Criteria for accrediting engineering programs, 2025–2026. abet.org
2. American Society of Civil Engineers. (2024). Policy statement 573: Artificial intelligence and engineering responsibility. asce.org
3. National Society of Professional Engineers. (2026). Artificial intelligence (Position Statement No. 03-1774). nspe.org

Founder

Usewright was founded by Daniel I. Adeniranye, PhD, known to students and colleagues as ProfDan. He is an engineering and computing education researcher and educator who studies how students use, limit, and decline generative AI, with recent work in Computers and Education Open and ASEE proceedings. Trained first as a mechanical engineer, he earned his PhD in Engineering and Computing Education at Florida International University. Usewright is his second education technology venture.

Usewright is independent and in its early stage, and we welcome collaborators: instructors, researchers, designers, and developers. Connect with ProfDan on LinkedIn.