With the rapid penetration of generative artificial intelligence in educational scenarios, AI programming assistants have gradually been applied in STEM programming classrooms. However, whether they enhance or weaken the development of students' computational thinking remains an unclear conclusion. Therefore, in technology-enhanced STEM education, how to leverage the enabling value of AI while maintaining the core boundaries of computational thinking development has become a key issue that current student programming education urgently needs to address. This study, based on the theoretical framework of technology-enhanced education and STEM education, adopts a quasi-experimental research design. It selects undergraduate students from different universities as research subjects, sets up an AI-assisted learning group and a traditional programming teaching group, and through a one-month teaching intervention, systematically explores the dual impacts of AI programming assistants on the computational thinking of undergraduate students. This study uses methods such as computational thinking scale, programming work analysis, classroom observation, and interviews to conduct evaluations in four dimensions: abstract thinking, algorithm design, problem decomposition, and debugging and error correction. The expected results aim to reveal the dual value and boundary conditions of AI tools in university programming education and propose reasonable teaching strategies that balance efficiency and thinking cultivation, providing empirical evidence and practical references for the high-quality development of STEM education under technology empowerment.

Introduction

In engineering education, placement readiness is often assessed primarily through academic performance; however, academic scores alone may not adequately represent a student’s preparedness for industry roles. Engineering graduates are expected to demonstrate practical exposure, technical competence, and career-oriented engagement in addition to theoretical knowledge. This study aims to develop a structured, data-driven framework to evaluate placement readiness among undergraduate engineering students and identify key predictors influencing employability preparedness.

Methods

An exploratory pilot study was conducted using data collected from 11 undergraduate engineering students through a structured self-assessment instrument. The questionnaire captured academic indicators (CGPA, backlogs), programming proficiency, coding practice intensity, internship participation, mini-project involvement, resume readiness, and hackathon participation. Categorical responses were converted into numerical values, and a Composite Placement Readiness Score (PRS) was calculated by aggregating weighted academic and experiential indicators. Descriptive statistics and correlation analysis were performed to identify relationships between individual factors and overall readiness.

Results

The mean PRS indicated moderate overall preparedness within the cohort. Correlation analysis revealed that academic performance (r ≈ 0.73), internship participation (r ≈ 0.47), and mini-project involvement (r ≈ 0.52) showed the strongest positive associations with placement readiness. Students with internship and project exposure consistently demonstrated higher readiness scores compared to those without such experience.

Conclusions

The findings suggest that placement readiness among engineering students is multidimensional and strongly influenced by experiential learning components alongside academic consistency. This pilot framework demonstrates the feasibility of structured educational analytics for early identification of readiness gaps and supports the integration of practical exposure within engineering curricula to enhance employability outcomes.

Introduction: In China, demand is growing for professionals who combine linguistic expertise with computational skills. Yet traditional language programs at science and technology universities often lack such integration. This study explores the feasibility of establishing a Language Intelligence major with a structured curriculum integrating linguistics, AI engineering, and domain applications.

Methods: This empirical study is based on first-hand analysis of a program offered by the authors’ own department. Three methods were applied. 1. Directed content analysis of six Chinese national policy documents (2018-2025) using pre-defined themes: AI-education integration and interdisciplinary talent development. 2. To assess labor market demand, we reviewed 2024 industry reports, which show NLP positions ranking first in AI role growth, with severe shortages of hybrid-skill candidates. 3. A single case study of a real-world program: the English AI Pilot Program at Harbin Engineering University. Data sources include program curriculum documents, course lists, expert evaluations, and student feedback collected directly from the program.

Results: Three findings emerged. First, policy supports “AI+X” education, but traditional language curricula lack mathematics, programming, computational linguistics, and corpus training-specific gaps in technical terminology processing and multilingual annotation. Second, market data confirm rapid NLP growth and a hybrid talent shortage. Third, the case program demonstrates feasibility: it compresses traditional language courses, adds math/programming, and offers specialized courses. Experts call it “precisely positioned at the interdisciplinary frontier”; students report that data mining and language intelligence training build core competencies.

Conclusions: A Language Intelligence major offers a promising response to labor market needs. The model—a tripartite curriculum (linguistic theory, AI engineering, domain knowledge)—provides a preliminary reference for other universities facing similar humanities–technology disconnects. However, the program has operated for only one year, and findings are exploratory. Longitudinal research is needed to validate long-term effectiveness.

Inclusive education has traditionally focused on providing support for learners identified with specific needs, often through specialized programs or separate systems of support. While these approaches were developed to improve access to education, emerging research in inclusive pedagogy challenges this model by reframing learner diversity as a normal and expected feature of classrooms rather than a deviation from a perceived norm. Drawing on the work of Florian and colleagues, as well as contemporary findings from learning sciences research, this presentation explores how the concept of variability in human development disrupts traditional bell-curve assumptions about ability and achievement. Instead of designing education for an “average learner,” inclusive pedagogy encourages educators to expand the capacity of everyday classrooms so that all children can participate meaningfully in shared learning experiences.

This session examines the implications of these ideas for early childhood and teacher education programs, particularly within increasingly multicultural and multilingual contexts. It highlights how traditional special education structures may inadvertently reproduce inequities by separating learners rather than strengthening inclusive classroom practices. Through discussion of research insights and practical examples, the presentation invites educators to reconsider how curriculum design, teaching strategies, and classroom environments can better reflect the natural diversity of learners. Participants will gain practical insights into how inclusive pedagogy can inform teacher preparation and contribute to more equitable learning environments for all children.

The educational process in the STEM concept is aimed at implementing interdisciplinary learning models (cross-curricular connections), through which students are given the opportunity to discover the logical connection between different “pieces” of knowledge taught in multiple scholarly disciplines. Cross-curricular connections are a way to achieve stable knowledge, refracted through the prism of different scientific fields

The present study focuses on the big idea of energy flow in nature and integrates biology, physics and chemistry in various experiments, forming one integral unit. The energy from a biological point of view is studied through the series of experiments involving the fundamental processes like photosynthesis, metabolism, movement and action. To study energy like a physical phenomenon, students have to conduct some experiments like the construction of a dynamo, wind turbine, and water turbine, measure the photovoltaic effect and discuss alternative energy sources. The chemical part of the experiments is related to hydrogen energy and how to use the hydrogen cell to move a car.

When the same lesson is presented from different perspectives, we enable students to be flexible, make connections between subjects, and work in a variety of circumstances. They understand concepts, phenomena, and events better when we provide them with different learning contexts and perspectives through which to explore them.

Background:
The Scientific Foundations of Medicine (SFM) is the first foundational course in our medical curriculum and is heavily rooted in biochemistry and molecular mechanisms. It is one of the earliest and most academically demanding courses in the first year, and as such, a significant proportion of students struggle with the volume and conceptual depth of the material. In contrast, a small subset of students consistently achieve top-tier performance. Understanding the learning strategies of these high-performing students may provide actionable insights to support future cohorts. This study aims to identify the self-reported study behaviors and learning strategies most commonly used by top-performing students enrolled in the SFM course.

Methods:
We designed a cross-sectional descriptive study targeting the top-performing students (>90% grade in the course, achieved by around 15 students) in the SFM course. Participants will complete an 18-item Likert-scale survey examining study structure, use of active learning strategies, resource utilization, lecture engagement, peer collaboration, test-taking approaches, and self-attribution of success. Items were selected to focus on modifiable study behaviors rather than fixed characteristics. Planned analyses will include descriptive statistics summarizing response distributions (means, frequencies, and percentages) for each survey item to identify commonly endorsed learning behaviors among high-performing students.

Results:
Data collection is currently underway. Results will be available prior to the conference and will include summary statistics of endorsed learning behaviors and patterns that characterize high-performing students.

Conclusion:
Identifying the self-reported strategies of top-performing students may inform targeted academic support interventions and early guidance for incoming medical students in this rigorous foundational science course. Findings should be interpreted in light of the study’s limitations, including the small sample size and the focus on a select group of high-performing students from a single course, which may limit generalizability.

Education as a strategic field is constantly called upon to reinvent itself, seeking dialogue with other actors and educational spaces; pedagogical innovation that considers digital transformation; collaborative and non-fragmented teaching based on real problems; and the responsible and interdisciplinary incorporation of technologies. In this context, methodologies aligned with these demands emerge, such as the STEM approach. It is from this approach that the research presented here is developed, with the objectives of stimulating students’ interest in science, enhancing new teaching methodologies, and contributing to the improvement of public basic education. The research is being developed in public basic education schools (early childhood education, elementary education, and high school). Considering Microscopy as a pedagogical tool and a means of articulating STEM disciplines, the project uses images produced at the microscopic level to mobilize students and teachers in the pursuit of innovative teaching, with the pedagogical and responsible use of technology. The methodology used is intervention research, in three stages: the capture, the digital processing, and the exhibition of the processed images. The first stage involves training students and teachers to use a pocket microscope attached to a cellphone or tablet; the second stage involves the use of a specific program for processing the captured images, carried out by the students; and the third stage involves the selection, discussion, and presentation of the processed images with the school community. As a result, a greater interest of the students in science and the pedagogical and responsible use of technologies can be observed, configuring a scenario of possibilities for the STEM approach in schools.

The highly specialized field of quantum technologies has established a foundation and a source of inspiration for integrating innovative educational practices. In this realm, we heightened the Natural Approach, as defined by Krashen and Terrell as a synonym conform to the naturalistic principles of second language acquisition. The emphasis of treating quantum mechanics as a naturalistic framework concerns our disposition to view it as a vehicle for communicating meanings acquired through the hierarchical structure complexity of containing inputs, transformations and associations of the targeted solution by stages. The assumption of a linguistic hierarchy consists of lexicon symbols considered of primary importance in the construction and interpretation of achieving quantum physics (QP) literacy readiness for progressing towards more specialized areas covering the full breath of QP knowledge. The principal tenets of Krashen and Terrell Natural Approach theory are aligned to a linguistic system claiming acquisition/learning in the natural way and the capacity to correct ourselves when we communicate QP knowledge. We construct on the linguistic system hypotheses a set of general design principles and a syllabus approach aimed at fitting the needs and interest of students while pursuing communicative goals at the end of a course to the required proficiency level.

In the face of rapid digital transformation, higher education institutions are increasingly expected to adapt to technological advances that impact teaching, learning, research, and governance. Although digital leadership has gained more attention in educational research, studies exploring how digital academic leadership is understood within academic communities remain limited, especially in emerging educational settings like Kazakhstan.
This study aims to examine how academics in higher education and school settings conceptualize and understand digital academic leadership. Their experiences offer valuable insights into the skills, opportunities, and challenges related to digital academic leadership in modern educational institutions.
The study used a qualitative phenomenological research approach to investigate the lived experiences and perceptions of academics about digital academic leadership. Phenomenology was chosen as the methodological framework because it emphasizes understanding individuals’ lived experiences and the meanings they assign to a specific phenomenon. In this research, the phenomenon examined was the concept and practice of digital academic leadership within educational settings.
The participants comprised 35 academics based in Astana, Kazakhstan. They were chosen through purposive sampling to include individuals who actively incorporate digital technologies into educational contexts. The group included professionals from a range of academic disciplines, many of whom held roles such as associate professors and lecturers at universities and other educational institutions. Their varied disciplinary backgrounds enabled the study to gain a broader understanding of how digital academic leadership is perceived across different fields.
Data were collected through open-ended questions, allowing participants to express their perspectives and experiences in detail. This approach enabled the researchers to obtain rich qualitative data reflecting participants’ authentic views on digital leadership practices, required competencies, and the role of digital technologies in educational leadership. The collected data were analyzed using content analysis, which involved systematic coding, categorization, and interpretation of participants’ responses to identify key themes and patterns.

Inclusive education is a key priority in early childhood education (ECE), ensuring equitable learning opportunities for students of determination (SOD). In the UAE, policies such as “School for All” promote inclusive practices, yet teachers face challenges in implementation due to workload pressures, gaps in practical training, and limited classroom support. This study examines ECE teachers’ perceptions, attitudes, self-efficacy, intentions, and concerns regarding inclusive pedagogy. Using a mixed-methods approach, data were collected through surveys and interviews with 17 homeroom teachers from Pre-K to Grade 2. Findings show that teachers generally hold positive attitudes and demonstrate high self-efficacy towards inclusion, but report significant concerns, particularly about managing severe disabilities and adapting curricula to diverse needs. Correlational analyses revealed that higher levels of concern are associated with lower self-efficacy and less favorable attitudes, while intentions to implement inclusive practices remain moderately positive and show a surprising positive relationship with concerns, reflecting teachers’ commitment despite challenges. Interviews highlighted challenges, including a disconnect between theoretical training and practical classroom realities, parental resistance influenced by cultural stigma, and insufficient professional development and support. Despite these barriers, most teachers demonstrate a willingness to collaborate and adapt when provided with adequate resources and guidance. The study emphasizes the need for targeted professional development, stronger administrative and specialist support, enhanced parental engagement, and improved home–school collaboration to better support inclusive pedagogy in early childhood settings.