Cite this Article

Mohamed Kasim, 2025. "Multimodal AI for Predicting how people Feel in Real Time on Educational Platforms", International Journal of Research in Artificial Intelligence and Data Science(IJRAIDS)1(1): 22-34.

The International Journal of Research in Artificial Intelligence and Data Science (IJRAIDS)
© 2025 by IJRAIDS
Volume 1 Issue 2
Year of Publication : 2025
Authors : Mohamed Kasim
Doi : XXXX XXXX XXXX

Keywords

Multimodal AI, Emotion recognition, Real-time prediction, Educational technology, Affective computing, Deep learning, Student engagement, Adaptive learning, Multimodal fusion.

Abstract

Smart schools now need to be able to recognise emotions since they affect how motivated students are, how individualised their learning is, and how well they learn. This article talks about a whole multimodal artificial intelligence (AI) framework that can predict how people will feel in real time on educational platforms. The recommended methodology integrates data from visual (facial expressions), audio (speech), physiological (heart rate, EEG), and textual (conversation or feedback) channels to dynamically figure out how students are experiencing throughout digital learning sessions. We employ deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms to put these different types of data together into one.

The best thing about our approach is that it can grasp intricate, multimodal inputs to gain a better idea of how learners are feeling. This is especially important in digital education, where it might be impossible to detect or hear emotional cues because there is no real person there. Teachers can modify the speed of the class, give useful feedback, or let teachers know when anything is incorrect with multimodal emotion recognition. Our technique is also resistant to noise and missing data since it changes the weights of each modality's contributions over time.

We test performance with benchmark datasets such as DAiSEE, EmoReact, and DEAP. These datasets have a lot of different multimedia and educational learning situations. Our results suggest that the multimodal framework works far better than unimodal baselines, especially when there are emotional cues that aren't apparent or don't match up. We also look at latency and computing efficiency to show that the model can work in real time without sacrificing accuracy.

We also explain how the proposed model can be applied in a practical classroom setting. The approach is part of a study Management System (LMS) that lets teachers examine dashboards in real time and gives students customised aid with their study. A pilot study with high school students found that engagement, emotional alignment, and satisfaction all increased higher.

We consider about privacy and ethics at every step of the development process. acquiring data involves acquiring permission, making it anonymous, and encrypting it. We also talk about how to make things clearer, give consumers more control, and eliminate bias so that AI may be used safely in schools.

This study talks about a real-time, scalable, multimodal AI method for forecasting how people would feel in school. By combining deep learning with cross-modal sensing, the framework can revolutionise the way we learn to be more empathetic, adaptive, and open to everyone. Our findings suggest that incorporating clever emotion-aware algorithms can help bridge the gap between online and in-person education, making digital learning environments more engaging and emotionally connected. has become a significant part of smart educational systems that has an impact on engagement, customisation, and learning outcomes. This study aims at a complete multimodal artificial intelligence (AI) system that can predict how people will feel in real time on educational platforms. The model that was suggested incorporates information from visual (facial expressions), acoustic (speech), physiological (heart rate, EEG), and textual (conversation or feedback) channels to figure out how students are experiencing while they are learning online. We employ attention mechanisms, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) to mix different kinds of input. When evaluated on standard datasets like DAiSEE, EmoReact, and DEAP, the results suggest that this method is far more accurate than unimodal methods. We also display a prototype that works with an online learning management system (LMS) and see how well it functions in the real world. The study finishes with a talk about the moral problems and impacts of adaptive schooling.

Introduction

Artificial intelligence (AI) has altered the way students learn, interact to each other, and obtain feedback on educational platforms. Emotion-aware learning is one of the most fundamental developments in this domain. It lets an intelligent system comprehend and respond to how learners are feeling in real time. Emotions have a huge effect on memory, motivation, focus, and brain growth. Traditional online learning methods can't tell whether pupils are bored, puzzled, or angry as well as human teachers can. So, utilising AI to guess how people feel is vital for improving learning outcomes and making students happier.

The rise of emotion-aware AI in schools is because affective computing, multimodal data processing, and deep learning have all gotten better. Most traditional e-learning systems just cared about conveying knowledge and not how the student felt. But research has shown that how students feel is very essential for how they connect with and remember what they learn. For example, a student who is bored or angry is less likely to learn well than one who is enthusiastic and motivated. To design schools that perform well and respond quickly, it's very crucial to be able to interpret these emotional indicators as they happen.

Multimodal AI systems use a range of input types, such as video, audio, text, and physical indications, to acquire a whole picture of how the student is feeling. Different types of communication convey different things. For example, facial expressions show how someone feels, tone of voice shows how tense or excited they are, body language shows how sure or unsure they are, and physiological indications show objective markers. When put together the right way, these data streams let you properly read emotions in real time, even when there is a lot of noise or uncertainty. For example, a student might say something that makes them angry but not reflect it in their body language or minor facial movements.

The COVID-19 pandemic and other worldwide events that forced schools to transition to remote and hybrid learning models also made it evident that we need digital education tools that take emotions into account. Many students claim they feel alone, uncomfortable, or unmotivated because they can't interact with other students in person. AI systems that can read emotions can see these undesirable tendencies and get virtual or real-life teachers to step in at the correct time. Being this sensitive makes learning more personal, which is good for both academic success and mental wellness.

This study points to a complex multimodal framework that was made only for schools to forecast how people will feel in real time. We speak about how to gather data, how to make models, how to put them together, how to test them, and how to think about ethics. We do three things: (1) we build a novel multimodal architecture that is best for real-time inference, (2) we test the model on three multimodal emotion datasets, and (3) we add the system to a working LMS to see if it works in the real world. We believe that this work makes it possible for AI tutors to actually understand students and assist them get more involved, tailor their learning, and do better. We want to close the emotional gap in digital learning and make technology more like the complicated reality of human education by accomplishing this. AI has revolutionised the way students learn, interact to one other, and obtain feedback on educational platforms. One of the most important new ideas in this discipline is learning that takes emotions into account. It allows a smart system see and respond to how students are feeling in real time. Feelings have a huge impact on memory, motivation, focus, and brain growth. Traditional online learning methods can't tell whether students are bored, confused, or angry like human teachers can. So, using AI to guess how people feel is vital for improving students' health and learning outcomes.

Multimodal AI systems use several types of input, such as video, audio, text, and physiological signals, to acquire a whole picture of how the learner is doing. diverse types of communication convey diverse points of view. For example, facial expressions show how someone feels, tone of voice shows tension or excitement, body language shows confidence or doubt, and physiological signals show objective markers. When put together the right way, these data streams let you see emotions in real time, even when there is a lot of noise or confusion.

This study presents a complex multimodal framework for anticipating people's feelings in real time that is made for schools. We speak about how to gather data, how to make models, how to put them together, how to test them, and how to think about ethics. We do three things: (1) we design a new multimodal architecture that works best for real-time inference, (2) we test the model on three multimodal emotion datasets, and (3) we connect the system to a working LMS to check if it works in real life. We believe that this study paves the way for AI teachers who can truly comprehend pupils and boost their participation, customisation, and outcomes.