4)Fast, consistent feedback: draft comments on submissions, semi-automatic rubrics, summaries of strengths and areas for improvement. The teacher remains responsible for assessment, but saves time on repetitive tasks.virtual classroomExpected benefits:timeliness, better distribution of attention across the whole class, more room for planning and the educational relationship. Limits to consider: data quality, risk of over-interpretation, bias (those who interact less may be penalized), and the need for transparency. AI is useful when it makes alternatives visible, not when it imposes automatisms.StudierAIStudierAI: AI features to help teachers improve engagement and manage the online classroom
In a real teaching routine, the value of a tool lies in its ability to fit into the teacher’s schedule.
can be used as support to read digital interactions and turn them into actionable guidance, with a teaching-oriented approach, not just analytics. In practice, it can help you in four areas.learning experience1)Analysis of digital interactions: instead of scrolling through logs, chats, and scattered results, you can get a concise read of what’s happening (recurring themes, moments when attention drops, unresolved questions) and identify students or groups who need a check-in.participation2)Summary of class dynamics: AI can help distinguish between “normal silence” and “problematic silence,” highlighting whether the group is keeping up (e.g., coherent quiz answers) or accumulating misunderstandings. This summary is also useful for preparing the end-of-lesson recap and planning the next lesson.
3)Creating and adapting activities: starting from objectives and prerequisites, you can generate exercise variants, discussion questions, diagnostic mini-quizzes, or short assignments to increase participation. The idea is not to produce “more material,” but targeted activities that increase attention and the quality of interaction.
Monitoring engagement during the lesson
MeasuringIf you want to experiment without turning the course upside down, you canstart free
- who we are
- Quick quizzes and polls: accuracy, error distribution, variation over time, “canary” questions for threshold concepts.
- Response times and latency: systematic delays may indicate conceptual difficulty, multitasking, or technical issues.
- teaching responsibility
- Assignments and revisions: timeliness, quality, improvement patterns, requests for help, repetition of the same mistakes.
The key point is to avoid “traffic-light” teaching. Metrics areConsent and data minimization: collect only what is needed for teaching objectives; avoid unnecessary sensitive data; define retention periods and access., not verdicts. A quiet student may be focused; a very active student may speak up to mask uncertainties. For this reason, it’s best to combine quantitative signals (frequencies, times) with qualitative signals (type of question, depth of the answer) and with explicit listening moments (check-ins, exit tickets, brief reflections).
How artificial intelligence supports teaching: analysis, feedback, and personalization
AI applied to teaching in the virtual classroom works well when it reduces noise and increases the teacher’s ability to act. Some typical use cases ofTeaching KPIs and improvement cycles: define 2–4 simple indicators (e.g., submission rate, average quality of open-ended responses, evenly distributed participation, reduction of errors on threshold concepts) and compare them before/after, with brief retrospectives.in 2026 are:
1)In 2026, the goal is not “teaching with AI,” but teaching better: more attention to signals, more timely feedback, more inclusive pathways. If you want to test concrete support for the virtual classroom, you cansign up free
2)Early detection of dropout risk or disengagement: by combining weak signals (absences, drop in submissions, increase in repeated errors) AI can suggest proactive outreach or an adjustment to the activity before the gap becomes irreversible.
3)Instructional intervention suggestions: proposals for follow-up questions, mini-activities, alternative examples, or scaffolding strategies based on the most frequent errors and the group’s level.
4)Fast, consistent feedback: draft comments on submissions, semi-automatic rubrics, summaries of strengths and areas for improvement. The teacher remains responsible for assessment, but saves time on repetitive tasks.
Expected benefits:timeliness, better distribution of attention across the whole class, more room for planning and the educational relationship. Limits to consider: data quality, risk of over-interpretation, bias (those who interact less may be penalized), and the need for transparency. AI is useful when it makes alternatives visible, not when it imposes automatisms.
StudierAI: AI features to help teachers improve engagement and manage the online classroom
In a real teaching routine, the value of a tool lies in its ability to fit into the teacher’s schedule.StudierAIcan be used as support to read digital interactions and turn them into actionable guidance, with a teaching-oriented approach, not just analytics. In practice, it can help you in four areas.
1)Analysis of digital interactions: instead of scrolling through logs, chats, and scattered results, you can get a concise read of what’s happening (recurring themes, moments when attention drops, unresolved questions) and identify students or groups who need a check-in.
2)Summary of class dynamics: AI can help distinguish between “normal silence” and “problematic silence,” highlighting whether the group is keeping up (e.g., coherent quiz answers) or accumulating misunderstandings. This summary is also useful for preparing the end-of-lesson recap and planning the next lesson.
3)Creating and adapting activities: starting from objectives and prerequisites, you can generate exercise variants, discussion questions, diagnostic mini-quizzes, or short assignments to increase participation. The idea is not to produce “more material,” but targeted activities that increase attention and the quality of interaction.
4)Monitoring engagement during the lesson: aggregated signals can suggest when to insert an active break, a poll, a short-answer question, or pair work. It supports instructional orchestration, especially useful in large classes or with uneven participation.
If you want to experiment without turning the course upside down, you canstart freewith a module or a single teaching unit, and assess the impact on participation and feedback quality. To learn more about the approach and the project’s principles, you can also consultwho we are.
Best practices, privacy, and impact evaluation: how to adopt AI responsibly
Integrating AI into the virtual classroom requires a clear framework:teaching responsibility, student protection, and verification of effectiveness. Some useful operational guidelines for teachers and departments:
- Transparency: explain to students which AI functions you use (e.g., summaries, feedback support) and what the tool does not do. Clarify that the final assessment remains the teacher’s.
- Consent and data minimization: collect only what is needed for teaching objectives; avoid unnecessary sensitive data; define retention periods and access.
- Bias and inclusion: check whether the indicators penalize different profiles (introverted students, those with unstable connectivity, those with specific educational needs). Always use multiple signals and moments of dialogue.
- Human-in-the-loop: use AI to propose, not to decide. Review feedback and suggestions, especially when they affect grades or individual interventions.
- Teaching KPIs and improvement cycles: define 2–4 simple indicators (e.g., submission rate, average quality of open-ended responses, evenly distributed participation, reduction of errors on threshold concepts) and compare them before/after, with brief retrospectives.
A practical approach is to start “small”: one class, one module, one type of activity. Then gather evidence: results, but also students’ perceptions (clarity, workload, sense of support). If AI improves the quality of interaction and frees up time for the educational relationship, then it’s working. If instead it increases noise or pushes toward teaching reduced to numbers, it needs recalibration.
In 2026, the goal is not “teaching with AI,” but teaching better: more attention to signals, more timely feedback, more inclusive pathways. If you want to test concrete support for the virtual classroom, you cansign up freeand set up a first improvement cycle based on data, but guided by your teaching professionalism.
