Hello everyone, for Assignment 3 I have imagined a mobile app that takes an intuitive pulse on your classroom’s emotions and understanding using your device’s internal microphone. An app like this would explore a shift in artificial intelligence’s focus on individual problem solving to supporting a collective/community. Please listen to my podcast below to find out how I envision such an app would work:
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I really enjoyed your podcast, especially the way you described that moment when the “classroom vibe” shifts and students begin withdrawing. The connection you made between post-pandemic anxiety, fear of being wrong, and students hiding behind their screens felt very accurate.
Your explanation of how ClassVibes would use ambient sound rather than recording individual voices was also thoughtful. It made the concept feel realistic while still protecting student privacy. I liked the examples you gave of the app picking up laughter, confusion, silence, or energetic discussion to help teachers adjust in the moment.
The idea of shifting AI from being a personal assistant to something that supports the collective classroom was a strong point. It reframed mobile intelligence in a way that feels more aligned with how teachers actually work.
I really enjoyed your podcast-style presentation. The production quality was great and the format made your idea easy to follow. What you are proposing feels genuinely helpful for teachers, especially as classrooms get more complex and students’ needs become harder to see in the moment. I can picture this tool helping teachers notice those quieter students who often cope their way through lessons without truly engaging. Your focus on using technology to support teachers, rather than forcing teachers to adapt to the tech, feels refreshing. I do wonder how well a class-level pulse captures individual students who may need attention, since groups can vary so much. Overall, this is a thoughtful and exciting forecast.
Hey Mark, love the podcast style. Well done!
I loved the scenarios as I can really relate and resonate to that feeling of knowing that your students have disconnected and disengaged during your lesson. One aspect I think could be more developed is what it will offer once it notices students have checked out, what could it do to help the teacher to mix it up or curve engagement?
As overall student learning needs and classroom complexity is seemingly becoming more pronounced, the expectations on teachers to make lessons that value all learning needs and styles grows heavier. Perhaps ClassVibes could generate alternative lesson options based on the vibes and subject content.
I think the idea is definitely innovative and useful especially for new teachers. I also like that it uses AI to capture collective data while still respecting privacy by not recording or tracking anyone individually. I’m just curious what kinds of situations it would catch that a teacher wouldn’t already notice on their own. If the room goes quiet or the energy drops, most teachers can already feel that shift right away. So I’m wondering how much ClassVibes would actually add beyond what teachers naturally pick up just by being in the room.
That said, I can see one area where it might be useful — keeping track of participation patterns so teachers don’t have to constantly tally who’s contributing, who’s disengaged, or who needs more support. Formative assessment around participation can be hard to manage, especially with bigger classes, so maybe ClassVibes could help in that way.
The podcast was very well done. Engaging and professional.
Although, I am not fully sold on this. It may introduce un-needed surveillance in classrooms. And as others pointed out, this could cause a some issues in practice.
I wonder if the the solution this problem is introduce to fix not be achieved in other ways? Smaller class sizes, proper teaching supports and training. More adult supports in the classroom for teachers … I tend to enjoy the idea of co-teaching!
But, of course, these are costly to implement; whereas a new app can be very easy!
I really enjoyed listening to your podcast about ClassVibes. The idea of using sound to read the overall mood of a classroom feels fresh and practical. It focuses on the whole group instead of tracking individual students, and that makes the approach feel much more respectful and less invasive. I can see how this could help teachers catch moments of confusion or low energy that are easy to miss when you’re busy managing a lesson.
I also liked how others pointed out the limits of sound alone. Classroom noise can mean different things depending on the subject, the activity, and even the culture of the students. Still, the core idea—giving teachers a quick, simple sense of the room—has real potential. It reminds me of how we already rely on small signals, like laughter, silence, or a shift in tone, but sometimes we don’t catch them in time.
For me, the most interesting part is how this could support classroom community. Instead of using AI for individual productivity, you’re using it to help the group learn better together. That feels like the right direction for future tools.
And honestly, if ClassVibes can help solve even 67 of the issues we face in class every day, I’m all for it.
I really like your idea of using classroom sound detection to identify student engagement, and allow educators to respond more quickly and support learners who may be struggling or disengaged. This is a strong and innovative direction. This concept also aligns with behaviourist principles and my own teaching experience that that timely feedback can shape learning behaviours and motivate them to learn.
However, I wonder whether sound detection alone is sufficient to accurately interpret students’ emotions or engagement levels. Like what have been mentioned by the others, classroom sound can be ambiguous, and different noises do not always directly reflect motivation or understanding. The additional thing that I think you could consider is what additional data points or contextual elements could strengthen the accuracy of the app—for example, patterns in participation, physical cues, or contextual metadata. Drawing on your own teaching experience or existing research on affective computing may provide useful guidance on how to classify engagement more reliably.
Overall, I like your idea and it has great potential, by adding a multi-modal or theory-informed layer from the research on learning could make the concept even stronger.
Hi Mark,
Thanks for sharing this episode! I really like the idea behind ClassVibes—using AI to pick up on the vibe of the whole classroom instead of tracking individual students feels smart and respectful of privacy. Teachers juggling so many things during a lesson could definitely use that kind of heads-up when students start to check out or get confused.
I love the idea that it listens to general classroom sounds—like laughter, silence, or frustration—and sends a gentle nudge to help teachers know when to tweak their lesson. It’s like having an extra set of ears and eyes without being intrusive.
As a teacher, I see ClassVibes as a wonderful tool to use as it can take some pressure off during those critical moments so we can focus more on what really matters — connecting with and supporting our students.
Hey Mark,
The idea of using AI to drive community, versus just using it as a personal assistant, is an interesting one, and it gives me a bit of a pause. We are already grappling with AI’s impact on individuals, and their propensity to trust AI implicitly and not challenge its answers. I understand the purpose of your app, as an assistant to teacher to help them read the vibe in the room and act accordingly. In what ways do you think this shifts the dynamic of the relationship between teacher and student?
I don’t mean to be too critical, but I think it’s necessary to ask ourselves where our boundaries are with these technologies. No doubt they will continue to become more ubiquitous as we learn what works with them and what doesn’t, I just generally like hearing other’s opinions and perceptions around “where the line is” with these types of technologies.
Do you think this would affect a teacher’s ability to naturally gauge a room’s vibe? Do you think students might behave differently if they knew they were being passively monitored and became attuned to what the different sounds from ClassVibes mean for their learning experience?
In any case, it’s a neat idea! My own A3 assignment was also about a technology that hopefully shifts our dynamic with AI.
Thanks for sharing.
Hi Mark,
Love the format, the transition noises are awesome.
I like how your app pays attention to the feel of a classroom without recording or transcribing what people say. The focus on energy, engagement, and atmosphere makes me think about how often those signals get missed, even by experienced teachers. I can see how this could help someone notice when a lesson has landed, when a discussion has taken off, or when things have gone flat.
From an HR perspective though, I couldn’t help imagining what something like this would look like in a workplace. It seems like it could create some problems and raises real questions about boundaries, monitoring, and what limits are we willing to push this to.
Hi Mark,
The premise of this app is very interesting. You mentioned in your podcast that this would be achieved through the difference in ambient noise, but I wonder how that would work in different classroom environments. For instance, I would imagine a culinary teacher or a mechanics teacher would be teaching in a very different environment than an English or math class. How would ClassVibe account for the differences in noise? However, it would be interesting to see during which parts of the lesson/ what activity typically results in different vibes in the class. What kind of strategies do you think ClassVibes might be able to suggest in order to alter the vibe of the class?
I really enjoyed the podcast! It made me wonder how emotional cues will be interpreted for students from different cultural backgrounds. For example, I once talked to a classmate who is from a former Soviet country, and they told me that in their culture, people often avoid smiling because it can be interpreted as deceptive.