EducatorGo through the first assignments and send everyone feedback.
Research Methods · Moodle
Done in the course
- 38 submissions reviewed against your criteria, the 3 most common mistakes collected for next week's class
- 38 personal feedback messages, no grades, and a reminder to the 4 students who haven't submitted
Feedback for Anna Kovács · draft, awaiting your approval
Your research question is clear and the questionnaire follows it well. In the sampling section, describe how you chose your respondents: we'll go through this together in next week's class.
Send the feedback?
Sent: 38 feedback notes and 4 reminders.
Try it on a practice courseUsed by educators and students semester after semester since 2023. With researchers at ELTE, we study, discipline by discipline, when AI genuinely helps learning.
It answers, and it gets things done
A study partner for students.
Students ask questions, practise and prepare for exams from the course material. The assistant shows which material it answers from, and asks back so the student thinks it through. How much it may help is the educator's call.
Less course admin.
Nitro does the work around the course in Moodle: it shows who is where, writes the messages, uploads assignments and quizzes. It works with the educator's own permissions, and only what the educator has approved reaches students.
Every policy one question away.
The assistant answers from the university's own policies and guides, citing the point it relies on. Everyone sees only what they could already access.
Where to start?
With one Moodle course
An educator tries Nitro on a practice course with fictional students. On their own university Moodle it can start after a short conversation.
We already run similar workflows in Canvas- and GitHub-based courses.
With one subject, for students
The educator provides the material and sets up the assistant; students ask questions from it through the semester. The conversation log shows how it is used, and at the end of the semester it shows whether to extend it to other subjects and faculties.
With one administrative process
We start with one set of policies and one team, for example the registry. If the university already has its own assistant or chatbot, we connect to it.
With a training session for staff
A practical session for educators, researchers and other staff, on their own tasks: what AI is worth using for, how to check the result, and what the university's AI policy asks for. It can also fit into the university's existing training.
By the university's rules
Every step that reaches a student is approved by a person, and the AI reaches only what the user could already access. GoSchool connects to Moodle and to the assistants already in place, and it can work with the model the university chooses. Only the student can see their own conversations.
Need AI for a different task?
Processing research material, internal information, course organisation or administration: we use the same tools for these too.
Bring a concrete task, and in the first conversation we'll show on it how much of it AI can do with the tools we already have.
Where it's already in use
GoSchool has transformed our university's approach to AI-assisted learning, making it seamless and engaging for both professors and students.
Let's start with a concrete need.
In the first conversation we look live, on one course or task, at what AI does, and agree who it makes sense to start with.