School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as ‘School Teachers' Electronics Practicals Upskilling Program (STEP-UP),‘ it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.

The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called “AI-based Research Companion (ARC)” has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.

This paper presents the System for Emotion and Engagement Recognition in Education (SEERE), a cutting-edge advancement integrating computer vision and deep learning tech-nologies to evaluate real-time student engagement through facial emotion recognition and eye tracking. SEERE, a transformative educational tool built on the robust YOLO V8 architecture, customizes the FER2013 dataset, making use of meticulously annotated emotion and eye position data. It goes further, es-tablishing a unique ‘concentration metric,’ a quantitative index of student engagement, bridging a gap in modern responsive teaching approaches. Higher concentration metrics signal height-ened student engagement, offering educators real-time data to adjust teaching techniques and feedback accordingly. The paper provides a thorough review of facial emotion recognition models, setting the stage for understanding the innovative strides made by SEERE. Detailed discussions on the prototype's design and architecture are followed by initial experimental results, reinforcing the system's validity and potential.

The decisions regarding a prospective career choice and the paths leading to it are life-changing actions for any student. Engineering students particularly have plenty of career opportunities to choose from after their graduation or even during campus placements. The wide gamut of opportunities may sometimes cause engineering students to end up in career paths that do not match their aptitudes, skills, and personality traits. Developing efficient career prediction and guidance systems exclusively for engineering students is a pressing priority. However, there is a scarcity of research studies on automated career prediction systems for engineering education settings. Against this backdrop, we propose a novel solution rooted in artificial intelligence titled Graphology-based Career Analysis and Prediction System (G-CAPS). Advanced graphology tools are employed to connect handwriting features with the personality traits of individual students. The Holland theory of vocational interests is adopted in G-CAPS to characterize and model individual career interests. Existing literature indicates that no such graphology-based prediction system was developed based on vocational personality traits. The G-CAPS model can be trained and tested using the handwriting samples collected from engineering students and working professionals with engineering degrees. Distinct handwriting features are captured and processed utilizing an array of Convolutional Neural networks (CNN). The system architecture development of the model and its working process is particularised in the paper. It is anticipated that the G-CAPS model can soundly address the career path selection issues of engineering students and graduates looking for a job. The innovative prediction system can be scaled to assist engineering students and graduates across the globe in selecting potential career paths most suitable for their specific character traits.

Gender typecasts have prevailed in higher education since time immemorial. Previous research studies have demonstrated the incongruity in gender representation in almost every higher education sector, including engineering education. Gender-specific variation in perceptions towards engineering education and the engineering profession is a significant area of interest. However, relatively little research has been conducted on gender differences in school students' attitudes and perceptions of engineering. In addition, no such previous study has surveyed students of schools on the rural outskirts. The current study intends to address this research gap by investigating the insights of school students who participated in the ‘Rural Students' Technology Enhancement Program (R-STEP).’ R-STEP was a technical training program organised by the IEEE Education Society (EdSoc) Kerala Chapter exclusively for less-privileged school students from the rural outskirts of Kerala, a southern state of India. The research study critically analysed the R-STEP participants' responses to a questionnaire survey. The questionnaire was in the regional language, Malayalam, comprising five questions each on engineering education and profession. A total of 220 school students from various districts of Kerala participated in the survey. While 62.5% of female survey respondents indicated that they want to be engineers in the future, it was a more significant 86% in the case of males. Rather interestingly, 16.1% of female participants reported that they do not want to be engineers, compared to only 3.1% of males. This research-worthy fivefold difference between male and female attitudes is critically deliberated in the paper, along with other findings from the study. The paper concludes with a few recommendations to address rural school students' gender-specific attitudes towards engineering.

The innate ability to recognize facial expressions and associated emotions is fundamental to human communication. Technology advancements have enabled computers to perform similar tasks to a considerable extent, opening versatile applications in diverse domains. In particular, Facial Emotion Recognition (FER) technology has recently been widely explored for investigating student engagement in classroom settings. While previous research studies mainly captivated the FER practice in engagement detection, far too little attention has been paid to the real-time emotional states of students during classroom interactions. In this regard, this paper introduces the In-Class Student Emotion and Engagement Detection System (iSEEDS), a novel AI-based approach for pinpointing learners' emotional states during classroom lectures. The iSEEDS employs Convo-lutional Neural Network (CNN) models for emotion detection and corresponding eye movement analysis. The system can help educators respond in real-time to students' emotional states and engagement levels. It can support responsive teaching by initiating remedial feedback in accordance with students' current emotions and engagement. A detailed literature review of existing emotion recognition models is presented as a background of iSEEDS development. Then the initial prototype model design and illustrative test results are discussed. Potential applications of iSEEDS and future research directions are also elaborated.

The COVID-19 pandemic has reformed the teaching-learning processes in engineering education across the globe. Virtual classrooms substituted physical classrooms with the widespread use of online meeting platforms. The proliferation of virtual classrooms not only paved the way for accelerated digital transformation but also brought back some elementary issues in engineering education. Many engineering students face difficulties in comprehending the fundamental concepts in their courses during virtual learning. As real-world engineering solutions depend on conceptual clarity, misconceptions of basic engineering principles need to be taken seriously. If not identified, analysed and corrected with constructive feedback, misconceptions on various engineering topics can create challenging obstacles in learning. Against this backdrop, this research study introduces a novel solution titled Classification of Students Misconceptions in Individualised Learning Environment (C-SMILE). The primary objective of the C-SMILE system is to examine the usefulness of personalised automated feedback to students to enhance their conceptual understanding by pinpointing their misconceptions. Besides, we propose a method by which students’ misconceptions can be effectively classified for every instructional objective in every engineering course using machine learning techniques. Our pilot-study results show that the proposed C-SMILE system can precisely classify students’ misconceptions in engineering education settings.

As the growth of science and technology accelerates, the prospect of engineering as a field of study and profession gains importance. Many school students prefer to study engineering in the future and work as engineers. Examining their views on engineering will help correct frivolous attitudes and proactively revamp engineering education. However, only a few studies have been conducted on secondary school students’ attitudes and perceptions toward engineering, especially from the schools located on rural outskirts. Against this backdrop, this study critically examines rural secondary school students’ attitudes and perceptions of engineering education and the engineering profession. The students were part of the Rural Student Technical Enhancement Program (R-STEP), conducted by the IEEE Education Society. The program was designed as a free-of-cost advanced technical training and skilling session for rural higher secondary students in Kerala, a southern state in India. The data collection for the study was performed by administering a questionnaire containing ten questions, among which five were concerning engineering education, and the other five were on the engineering profession. The questionnaire was prepared in the regional language (Malayalam), considering the respondents’ age group and background. A total of 225 responses were collected from students from various districts of Kerala. The questionnaire responses were then critically analyzed to interpret students’ opinions on engineering education and profession. The research findings show that nearly 60% of rural higher secondary school students sincerely aspire to become engineers. It is also observed that about 50% of students acknowledge that engineers should exhibit problem-solving skills. Another noticeable finding is that about 80% of students accept the significance of practical laboratory-oriented learning in engineering education. On the other hand, more than 30% of students are confused about the significance of mathematics in engineering. Another 30% of students even observed that mathematical aptitude is not obligatory for engineering education. More insights from other questions identified students’ attitudes and perceptions toward the engineering profession. The study concludes with a few curative suggestions and recommendations to enhance rural school students’ awareness of engineering education and profession.