Language barriers remain one of the significant challenges to equity in education in the increasingly internationalised educational environments. Students who are not proficient in the instructional language often experience knowledge gaps, high cognitive load, reduced comprehension, and limited participation. The project aims to design and implement a real-time speech transcription and translation system that converts instructors’ spoken discourse into translated text aligned with individual learners’ language preferences. The system is intended for deployment in classrooms, lecture halls, and digital learning environments to support multilingual student populations and enhance accessibility for learners with hearing impairments through immediate text-based mediation.

Azure Cognitive Services handles the transcription-translation inferences, while the system development for the backend, and a user-friendly frontend interface for the student-client to access the system are built using the Python programming language and  JavaScript, respectively.  The project will produce deployable prototypes of an adaptive language mediation system suitable for classroom use, able to effectively support student comprehension, reduce cognitive load, and learning disparities in multilingual learning environments, hence promoting inclusive education.  Practical guidelines will also be provided to interested educational institutions on how to integrate the real-time language mediation system into their learning ecosystems.

Keywords:- Language barriers,  AI-based language support, Speech recognition, Inclusive education,  Higher education, Language mediation.

Problem statement: International students—including exchange and Erasmus participants who are learning in a language different from their first often face learning disadvantages such as high cognitive load, covert knowledge gaps, reduced academic performance, and deprivation of classroom participation because they struggle to fully understand the language of instruction. Although current technologies for language mediation have advanced significantly, effective classroom applications are still limited, particularly in terms of performance, contextual accuracy, glossary simplification, and the level of linguistic personalisation required by individual learners. Even when students have conversational proficiency, learning in an unfamiliar language can create serious challenges.

• System Design and Architecture: Design a modular architecture integrating automatic speech recognition (ASR), neural machine translation (NMT), and a responsive user interface. Define system requirements based on multilingual classroom use cases and accessibility standards.
• Prototype Development: Implement a functional prototype that uses real-time speech processing and cloud-based or local translation APIs. Integrate user interface components for language selection, transcript display, and note-taking.
• Pilot Deployment: Deploy the system in a controlled classroom or digital learning environment. Collect live speech data to evaluate transcription accuracy, translation quality, and latency.
• Evaluation and Validation: Assess system performance using quantitative metrics (e.g., Word Error Rate, translation accuracy, response time). Gather qualitative feedback from students and instructors regarding usability, comprehension support, and accessibility impact.
• Iterative Refinement: Refine the system based on technical performance data and user feedback. Optimize linguistic accuracy, interface usability, and pedagogical alignment

• Functional Real-Time Language Mediation System: A validated prototype capable of delivering accurate, low-latency speech transcription and translation across multilingual learning environments.
• Improved Comprehension and Inclusion: Enhanced learner understanding through immediate language support, particularly for multilingual students and learners with hearing impairments.
• Increased Learner Autonomy and Engagement: Greater control over language preferences and learning pace, fostering active participation and self-regulated learning.
• Measurable Accessibility Impact: Demonstrable improvements in accessibility metrics, comprehension performance, and user satisfaction based on pilot evaluation data.
• Scalable Educational Technology Framework: A deployable model adaptable to diverse institutional contexts, supporting inclusive and technology-mediated pedagogy.

Presents real-time translated or mediated language content for active learning and comprehension.

Enables learners to revisit the previous five minutes of instruction for reinforcement, clarification, and improved comprehension.

Enables multilingual interaction and language selection for inclusive communication.

Controls live communication sessions for interactive language engagement.

Removes displayed transcripts to maintain focus and reset learning tasks.

Enhances readability by automatically following live transcript updates.

Facilitates content retrieval and reflective review of communication exchanges.

Supports readability and accessibility through adjustable text presentation.

Improves visual accessibility and user comfort in different environments.

Enables record keeping, revision, and post-session learning analysis

Encourages active learning through reflection, annotation, and knowledge capture.

Supports organization, revision, and management of learner-generated notes.

Tracks instructional time to support time management and structured learning engagement.

Provides feedback on system connectivity and session readiness.

Abstract

Affective and social dimensions significantly influence learning effectiveness, yet they remain under-integrated in higher education instructional design. Existing strategies largely prioritise cognitive performance metrics, relying on intuition and standardised frameworks rather than affective data analytics. This project proposes a socially-aware affective computing system implemented on an STM32N6-series microcontroller to capture, process, and interpret learners’ emotional states and social interaction patterns in real time. The system leverages these insights to inform adaptive instructional strategies that enhance personalisation, engagement, and inclusive digital learning environments in higher education. This is an integral part of the global efforts on inclusive and quality education in support of UN SDG 4.

Keywords: Learning design, HCI-Education, Affective computing, Embedded systems, Social interactions.

Problem statement:  As pedagogical activities are increasingly digitalised and automated, the effective implementation of socio-affective development of learners in the instructional strategies in higher education is being challenged and underrepresented. As we continue to rely on digital tools, the gap between the need for socio-emotional learning and its representation in learning resources continues to grow. Additionally, the ubiquitous traditional e-learning platforms, which often offer static content, frequently fail to engage diverse learners, leaving them underchallenged or overwhelmed by not addressing their individual needs.  These are emerging problems in digital educational research.

• System Architecture Design: Develop an embedded affective computing framework using the STM32N6 microcontroller integrated with multimodal sensors (e.g., facial expression, voice tone, or interaction data). Define affective and social indicators relevant to instructional adaptation.
• Data Acquisition and Modelling: Collect real-time emotional and social interaction data during learning sessions. Apply lightweight machine learning models optimised for edge processing to classify affective states and social engagement levels.
• Adaptive Instructional Engine Development: Design rule-based or ML-driven adaptive mechanisms that adjust instructional strategies (e.g., pacing, feedback type, content difficulty) based on detected states.
• Pilot Testing and Evaluation: Deploy the system in a controlled higher education setting. Evaluate accuracy of affective detection, system responsiveness, and impact on engagement and learning outcomes through quantitative and qualitative measures.

• Embedded Affective Detection Prototype: A functional STM32N6-based system capable of detecting and classifying learners’ affective and social states in real time.
• Adaptive Instructional Response Mechanism: An operational model that dynamically adjusts instructional variables (e.g., pacing, feedback, content difficulty) based on affective data.
• Improved Engagement Indicator: Observable increases in learner participation, sustained attention, and interaction quality during pilot implementation.
• Empirical Validation Data: Quantitative and qualitative evidence demonstrating the feasibility, accuracy, and pedagogical relevance of socially-aware adaptive instruction.