project-based learning

This work addresses the growing disconnect between software engineering education and industry practices, as academic curricula have lagged behind the rapid adoption of emerging technologies such as large language models (LLMs) and Model Context Protocol (MCP). To bridge this gap, the study introduces a novel pedagogical framework that synergistically integrates LLMs and MCP into the curriculum through intelligent programming assistance, engineering simulation platforms, and university–industry collaborative internships. This approach fosters a collaborative learning environment closely mirroring real-world industrial workflows. Empirical results demonstrate significant improvements in students’ programming proficiency, complex problem-solving capabilities, and competence in leveraging AI-powered tools. By aligning academic training with contemporary industry demands, this initiative effectively narrows the theory–practice divide and catalyzes a paradigm shift in software engineering education for the AI era.

K–12 teachers face persistent challenges in implementing project-based learning (PBL), including complex instructional design, inefficient process management, insufficiently personalized assessment, and imbalanced teacher–student roles. Method: This study introduces a teacher-driven co-design paradigm for LLM-powered educational tools, grounded in iterative cycles of teacher interviews, collaborative design workshops, and wireframe prototyping—integrating educational technology principles and human–AI collaboration theory. Contribution/Results: We developed the first empirically grounded, PBL-specific LLM design guideline that simultaneously supports teacher professional development, embeds ethical guardrails, and ensures classroom feasibility. The study distills four actionable, implementation-oriented design principles for LLM-augmented PBL, specifying mechanisms for resource adaptation, boundaries for ethical AI use, and pathways for sustained pedagogical impact—thereby establishing an evidence-based theoretical framework and practical co-design methodology for AI-enabled educational tool development.

Students lack structured, critical, and practice-oriented large language model (LLM) literacy. Method: We redesigned a post-CS1 course to systematically integrate four modules—LLM fundamentals, hands-on tool usage, ethics discussion, and reflective practice—and introduced a dual-strategy framework: “disclosure mechanisms” (requiring transparent AI use) and “validation training” (cultivating systematic verification of LLM outputs). Pedagogical approaches included LLM toolchain exercises, interactive classroom activities, pre-/post-intervention surveys, explicit critical thinking instruction, and an AI-augmented problem-solving framework. Contribution/Results: Students demonstrated deeper technical understanding, more deliberate and ethically grounded LLM usage, significantly enhanced validation awareness, and improved human-AI collaboration competence. The curriculum design is discipline-agnostic and offers a scalable, responsible pedagogical paradigm for computing education in an AI-integrated era.

The integration of AI into professional practice necessitates interdisciplinary AI curricula in higher education, yet empirical research and collaborative mechanisms remain insufficient. This study targets undergraduate engineering education and employs a mixed-methods approach: quantitative curriculum mapping to assess AI competency coverage, complemented by focus group interviews with multiple stakeholders (educators, students, industry professionals), with triangulation of qualitative and quantitative data. Its key contribution is the first systematic comparison of perceptions of curriculum quality between educators who co-designed AI courses and those who did not—revealing that deep educator involvement significantly enhances perceived course quality and industry alignment. Results indicate that embedding authentic industry requirements and fostering educator-led collaborative curriculum design are critical for improving the effectiveness and scalability of interdisciplinary AI courses. The study offers a reusable theoretical framework and practical paradigm for AI-integrated curriculum reform.

This study investigates the impact of integrating large language models (LLMs) into requirements engineering (RE) education on students’ learning experience, perceived benefits, and challenges in practice. Employing a cross-institutional empirical design, comparative experiments were conducted concurrently at two universities, embedding LLM-assisted instruction in both individual assignments and team-based agile projects, and evaluating outcomes via mixed-method analysis—including surveys and qualitative thematic analysis. Its key contribution is the first systematic comparison of LLM integration across distinct pedagogical contexts, yielding a novel “Contextualized AI Integration Framework.” Results indicate that LLMs significantly enhance students’ conceptual understanding and practical efficiency in requirements elicitation and documentation; however, they also expose critical challenges—including academic integrity risks, diminished critical thinking, and overreliance on AI. Accordingly, the study proposes pedagogical design principles and evidence-informed practices that balance AI augmentation with foundational competency development.

This study addresses the overemphasis on syntactic instruction and the neglect of soft skills in traditional introductory programming courses. To bridge this gap, the authors integrate a project-based learning (PBL) framework into a “Programming Fundamentals” course, using 2D maze game development as the central project. The approach incorporates a multimodal assessment system—comprising live coding demonstrations, technical screencasts, formal presentations, and peer evaluations—to explicitly embed communication, critical thinking, and collaboration into the core evaluation criteria. Empirical results indicate that this pedagogical model not only significantly enhances students’ mastery of foundational programming concepts but also effectively cultivates professional competencies. The proposed framework offers a scalable and systematic paradigm for computing education that holistically balances technical proficiency with essential workplace-ready skills.

This study addresses the prevalent fragmentation in AI education, where technical knowledge, societal impact, and workplace competencies are often taught in isolation. To bridge this gap, the authors propose an innovative curriculum mapping framework that systematically integrates three dimensions: AI technical foundations, societal harms, and professional competencies. Leveraging both institutional course offerings and an external repository of 335 registered courses, the framework enables cross-curricular content analysis. An initial analysis of six courses reveals relatively comprehensive coverage of technical content, uneven attention to societal harms, and minimal explicit assessment of workplace competencies. The proposed framework thus offers both a methodological foundation and empirical evidence for designing holistic AI literacy education that cultivates responsible agency alongside technical proficiency.

This study addresses the critical challenge of effectively integrating technical competencies with humanistic literacy in STEM education to cultivate interdisciplinary talent. The authors propose a staged, cross-disciplinary pedagogical approach that systematically integrates core technical concepts—such as information entropy and cybersecurity—with artistic practices including music composition, video production, game design, and Oxford-style debating. Implemented across multiple science and engineering institutions, the method was evaluated through a mixed-methods framework combining quantitative metrics (academic performance) and qualitative data (student feedback). Findings demonstrate significant improvements in both learning outcomes and classroom engagement, affirming the approach’s efficacy in bridging the divide between the sciences and humanities and its potential for broader educational adoption.

This work addresses the challenge undergraduate students face in deeply grasping core concepts of parallel and distributed computing (PDC) due to limited access to authentic high-performance computing (HPC) environments. To bridge this gap, the study introduces, for the first time in undergraduate instruction, systematic hands-on engagement with the real-world supercomputing platform HiPerGator. Students undertook structured assignments implementing and optimizing matrix multiplication in both Python and C, leveraging POSIX threads and OpenMP, while navigating job scheduling, core allocation, and performance tuning. Multi-year course evaluations across three consecutive offerings demonstrate that this approach significantly enhances students’ conceptual understanding of parallelism and multithreading, as well as their practical implementation skills, thereby affirming the pedagogical efficacy and innovative value of integrating genuine HPC infrastructure into undergraduate PDC education.

This study addresses the gap between existing Bodies of Knowledge (BoKs) in computing and their effective translation into assessable, competency-oriented curricula. The authors propose an innovative competency-mapping methodology that systematically aligns BoKs with a structured competency framework, resulting in a five-year engineering curriculum encompassing 23 core competencies, organized into five thematic modules and three specialization tracks, and integrating mandatory work-integrated learning projects. To support explicit linkage, collaborative maintenance, and continuous evolution of knowledge-to-competency mappings, the authors developed ISANUMpedia—a semantic web–based collaborative platform. Implemented in the ISANUM engineering degree program, this approach successfully mapped 494 knowledge topics to the 23 competencies, significantly enhancing the curriculum’s professional relevance and assessability.