While many of our conversations have focused on what the generative means of AI for student work and learning results, there is another question that teachers are asking – often individually and discreetly: how can we take advantage of AI in our own academic and administrative work? And more importantly, should we?
The answer, I believe, lies in the use of AI to help clean the space for the work that we can do – collaboration, connection and critical advice that makes education transformative.
This does not mean that we simply use AI as a crutch to respond to emails or summarize the meetings. In fact, I believe that the real promise of AI has just used it, in The words of Ethan Mollick, as a “real intellectual partner”, “ Whoever can improve class discussions, help create engaging educational documents and even help develop sets of sophisticated problems or simulations that previously required in -depth preparation time. As Mollick says, “the objective must go from the automation of tasks to increased capacity.”
AI offers many potential applications for teachers’ work. While teachers should continue to prioritize The importance of maintaining human connection, empathy and support in our educational practiceWe must consider other ways in which AI can increase our work. Perhaps a way is in the design of our courses, the assignments and activities that track students progress through the content and the results. But rather than asking the AI to develop prompts or notes for us, we can use AI as a tool to help develop our work in a surprising way.
Works in theory, vacillating in practice
We all fell in love with this question of key discussion or this prompt written assignment that sparkles in class. Despite our best intentions, we may not provide enough information, or we do not anticipate a blind spot which leads the students to sterile paths. One of the challenges of the course design is that all our work may seem perfectly clear and effective when we are to the knees in the design process, but everything collapses in a way when it is deployed in nature. From simple misunderstandings to false complex ideas, these problems are generally not revealed until we see the real work of students – often when it is too late to avoid frustration.
The filling of this gap requires an iterative refinement, recognizing that what works in theory or under controlled conditions requires tests, adaptation and continuous improvement in the real world. It is not only a question of conceiving something that works in the laboratory, but of ensuring that our conceptions are resilient, adaptable and sufficiently reactive to prosper in nature.
Although there is no substitute for real world tests, I started wondering if AI could help this iterative refinement. I didn’t want the AI for the refine or modify my prompts. I wanted to see if I could tamp the AI to model hundreds of responses from students to my invites in the hope that this process could give the kind of insight that I was too close to see.
The process: Test of attribution stress assisted by AI
After having experienced systems like Claude and Chatgpt, I discovered that they can effectively analyze and refine guests to writing thanks to the creation of simulated students’ responses. The basic approach works like this. First, provide AI information on your course and the key characteristics of your student population. Then share the assignment prompt. The AI generates internally several simulated responses simulated through different levels of competence. Afterwards, it provides a complete analysis identifying potential problems and opportunities.
You can specify that the analysis includes current erroneous interpretations that students could do or any structural or organizational challenges in the prompt. However, AI can also identify content development models and potential problems as well as specific to the population according to your student demographic data. Finally, AI can even suggest refinements at the prompt.
See what you don’t see
To test this approach, I downloaded a personal narrative prompt that asks students to connect their life experiences to their academic objectives – a current mission in first -year writing lessons.
AI analysis has revealed several dead angles in my rapid design. For example, I had not thought about how non -traditional students could fight with the “major language choice”, because many are career changes. The responses modeled by the AI also revealed that students may find it difficult to transmit between personal narrative and academic analysis sections. The most precious was to see how different students of students could interpret the same instructions. Career changes could focus too much on work experiences, while others may have trouble sharing personal information. These ideas allowed me to add clarifying language and support the material before real students meet these challenges.
The entire process lasted around 30 minutes, but potentially saved hours of students’ confusion and clarification of teachers. Of course, AI’s responses are not identical to human students’ responses, and we must be careful to consider AI as an infallible expert or an absolute source of truth. But used as an additional objective during the development of missions, this approach can grant courses a different perspective, which triggers precious information and potentially reduces the workload.
If you want to try this approach yourself, Here is a model prompt you can use with AI systems.
Course design multiplier
This process allowed me to develop targeted support materials for predicted problem areas before students fight, the creation of proactive scaffolding in the design of the courses from the start. And by sharing ideas acquired thanks to the analysis of the AI, the departments could collectively improve the practices of the design of the assignment, in particular the value of the multisection courses where consistency is important. Over time, we could build a practical library of “what works” from which teachers could draw, including analyzes explaining why certain missions succeed with populations of particular students and learning objectives.
Analysis of AIF assigned assignments offers a promising tool that respects our expertise while expanding our ability to anticipate the needs of students. Although technology is not perfect and will never replace the information obtained thanks to the direct interaction of students, it offers a precious perspective which helps to identify the dead angles before the students meet them. This represents only one way to implement AI can help us do more of what matters: creating significant learning experiences. Using AI for predictive work of the design of the mission, we release more time and energy for deeply human work to guide and connect with our students – the work we can do.
Dr. Nathan Pritts is a leader in higher education, specialized in the development of faculty, educational innovation and the integration of emerging technologies in education and learning. As a teacher and program president for first -year writing at the World Campus at the Arizona University, he has directed initiatives in the strategic implementation of online learning technologies, training programs for complete teachers and the creation of evolutionary interventions to support both professors and students in online environments. As an author and researcher, Dr. Print has published widely on subjects such as digital pedagogy, the design of IA -improved study programs, evaluation strategies and the future of higher education.
(Tagstotranslate) Courses design (T) Conception of AID (T) AI (T) Pedagogical Design (T) writing prompts
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