Starting a New Semester? Here's How to Set Up Your AI Tutor in a Weekend
The semester starts in ten days. You have lectures to finalise, tutorial sheets to update, a course outline to upload, and three emails from students who want to know if the assessment weightings have changed. The last thing you have time to do is learn a new piece of technology.
But here is the thing: setting up an AI teaching assistant is not a project that requires weeks of training or an IT team on standby. Done with some intention, it is a weekend project, roughly the same amount of time it takes to revise a set of tutorial answers or update your slides from last semester. The difference is that the AI assistant, once set up, keeps working all semester. It answers student questions at 11 p.m. It handles the same question for the fortieth time without losing patience. And it draws only on the material you have given it.
What follows is not a technology explainer. It is a practical guide for educators who already know they want to try this and simply need to know where to start.
Before You Open Any Tool: The Materials Audit
The most important hour you will spend on your AI setup will not involve any technology at all. It involves sitting with your course and asking a specific question: what does this AI actually need to know to teach my students well?
An AI teaching assistant is only as good as what you give it. If you upload a poorly organised collection of old slides, the AI will give poorly organised, hard-to-navigate answers. If you upload clear, current, well-structured course materials, the AI will reflect that quality back to your students.
Before you begin, gather your core course documents. This typically includes your course outline or syllabus, your lecture notes or slides, any assigned readings or textbook chapters you can legally share, your past tutorial sheets and answer keys, and any assessment rubrics you want students to understand.
Then do a quick audit. Ask yourself:
Is this material current? If your lecture notes still reference a 2019 case study that you stopped using two years ago, remove it before uploading. Outdated material confuses students and generates incorrect AI responses.
Is this material complete? An AI teaching assistant cannot fill gaps it does not know exist. If your notes assume foundational knowledge from a pre-requisite course, consider including a brief summary of that material.
Is this material yours to share? If you are using copyrighted textbook content, check your institution's licensing terms before uploading. In most cases, your own lecture notes, tutorial sheets, and original assessments are straightforward to include. Third-party content requires more care.
This audit typically takes 30 to 60 minutes. Do not skip it. It is what separates an AI assistant that gives your students confident, accurate responses from one that confidently gives them wrong answers.
Saturday Morning: Upload and Configure
With your materials in order, the actual setup process moves quickly. Using Walter as the worked example here (because it is designed specifically for this workflow), the steps look like this.
Start with your course structure. Before uploading any content, define the shape of your course inside Walter. Give it your course name, your institution, and your learning outcomes: the three to five things you genuinely want students to be able to do by the end of the semester. These outcomes do more than organise the content. They tell Walter what it is trying to help students achieve, which shapes how it responds when students ask questions.
Upload your materials in order of importance. Start with your lecture notes and tutorial sheets, as these are the documents students will ask about most often. Then add your assessment rubrics and course outline. If you have past exam papers with worked solutions, add those too. Walter indexes all of this and makes it searchable.
Set your scope. This is a step that many educators skip in their first setup and later wish they had not. Walter lets you define the boundaries of what it will and will not help students with. Do you want it to help students with assignment questions, or redirect them to office hours? Do you want it to give direct answers or to Socratically guide students toward their own answers? These are pedagogical choices, not technical ones, and they matter enormously.
If you are teaching a programming course and you want students to work through debugging problems rather than receiving solutions, tell Walter that. If you are teaching a law course and you want students to analyse cases independently before checking their reasoning, configure Walter to prompt reflection rather than deliver answers. The AI follows your instructions.
This configuration stage typically takes one to two hours for a well-organised course. At the end of it, you have an AI teaching assistant that knows your content, understands your learning objectives, and operates within the boundaries you have set.
Saturday Afternoon: Test Before Your Students Do
This step is non-negotiable. Before you share the AI assistant with a single student, you need to talk to it yourself, as a student would.
Ask it the questions your weakest students ask. Ask it the questions your strongest students ask. Ask it questions that are slightly outside the scope of the course, and see what it does. Ask it something you know is wrong, and see if it pushes back or capitulates.
What you are looking for in this testing phase:
Accuracy. Does the AI give correct answers on the core concepts of your course? If it is getting something wrong, the most likely reason is a gap or ambiguity in your uploaded materials. Go back to the source document, clarify the relevant section, and re-upload.
Tone. Is it responding in a way that feels appropriate for your students? If your course is highly technical and the responses feel too casual, or if your course is introductory and the responses feel intimidating, adjust your configuration.
Scope discipline. When a student asks something outside the course (say, they ask Walter to write their assignment for them), does it decline and redirect appropriately? Test the edges of what you have permitted. The first time a student probes those edges should not be your first time seeing how the AI responds.
Gaps. If the AI says "I don't have information about that" on a topic you know is central to your course, something is missing from your uploaded materials. Track down the relevant section and add it.
Expect this testing phase to take two to three hours. It is the most illuminating part of the process: you will learn a great deal about your own course materials by watching the AI try to use them.
Sunday: The Pre-Launch Checklist
By Sunday, you should have a configured, tested AI teaching assistant. Before you introduce it to students, run through a short checklist.
Have you written introduction language for your course portal? Students need to know what the AI assistant is, what it is for, and what it cannot do. A short paragraph in your course outline, something like: "This course includes access to an AI teaching assistant called Walter, which can help you with questions about course content, tutorial problems, and assessment preparation. Walter draws on our course materials and is not a substitute for lectures, tutorials, or office hours," sets the right expectations from day one.
Have you decided how you will monitor its use? Most AI teaching platforms for education include usage analytics: how many questions students are asking, which topics generate the most queries, which questions the AI is struggling to answer. Set aside 15 minutes per week to review this data. It is some of the most useful real-time feedback you will ever get on where your students are confused.
Have you thought about academic integrity? AI tutoring and AI-assisted cheating are different things, but students do not always see a clear line between them. Be explicit in your course materials about what constitutes appropriate use. Using Walter to understand a concept or check your reasoning is learning. Using it to generate your assignment answers and submit them as your own is academic misconduct. Say this plainly. Students will respect clarity more than vagueness.
Have you given yourself a review date? Set a calendar reminder for three weeks into the semester to revisit your Walter configuration. By then, you will have real data about what students are asking and where the AI is falling short. That is the best possible basis for improving the setup.
What the First Few Weeks Will Teach You
No setup is perfect. The first few weeks of student use will surface things your testing did not catch: a topic that generates confused questions, a type of question the AI handles awkwardly, a student population that uses the tool very differently from how you imagined.
Treat this as signal, not failure. The educators who get the most from AI teaching assistants are not the ones who set it up perfectly on the first attempt. They are the ones who pay attention to what the data is telling them and adjust.
Watch for the questions your AI cannot answer well, as they are usually pointing to gaps in your course materials or unclear explanations in your lectures. Watch for the questions that appear repeatedly at 11 p.m. before assessments, as they are telling you what your students are genuinely anxious about and what might need more airtime in class.
An AI teaching assistant, used thoughtfully, is a window into how your students experience your course. Most educators never get that feedback in real time. Use it.
One More Thing: This Is Still Your Course
There is a version of AI-in-education anxiety that worries AI will make teachers redundant. It is worth being direct about why that anxiety misunderstands what a well-configured AI teaching assistant actually does.
Walter knows what you put into it. It teaches what you have decided matters. It reflects the learning outcomes you have defined and the boundaries you have set. It is a highly capable, tireless extension of your course design, not an autonomous replacement for it.
Your students still need you to explain why the concepts matter, to model expert thinking, to challenge them in ways that require genuine intellectual engagement, and to make judgements about their growth that no algorithm is equipped to make. The AI handles the repetitive, scalable parts of teaching: answering the same tutorial question for the fifteenth time, explaining a concept three different ways until one of them lands, being available at midnight before a submission deadline. You handle everything that actually requires a human.
That division of labour, set up in a weekend, can make a significant difference to both you and your students before the first lecture of the new semester even begins.
Further Reading
Sources
Noodle Factory. Walter AI Teaching Assistant: Platform Documentation. 2026.
Bloom, B. S. (1984). "The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring." Educational Researcher, 13(6), 4–16.
Selwyn, N. (2022). Education and Technology: Key Issues and Debates (3rd ed.). Bloomsbury Academic.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Trust, T., & Whalen, J. (2020). "Should Teachers Be Trained in Emergency Remote Teaching? Lessons Learned from the COVID-19 Pandemic." Journal of Technology and Teacher Education, 28(2), 189–199.
OECD. Education at a Glance 2025: OECD Indicators. OECD Publishing.
Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press.
