Certificate in Higher Education Teaching | Gdańsk University of Technology

Radosław Zając, Warsaw University of Technology

Course description

Higher education is transforming from knowledge transmission to value creation. In this new paradigm, students become prosumers - co-creators of learning value - while instructors act as designers of learning experiences. The course introduces modern didactic frameworks that merge concepts from communication and psychology. Drawing on the Teaching as Selling approach, Kolb’s experiential cycle, and Bloom’s taxonomy, participants will learn how to design courses that integrate students’ prior knowledge, diverse motivations, and measurable learning outcomes.

The course emphasizes practical design and iterative improvement of teaching processes, helping participants develop a reflective, evidence-informed teaching practice aligned with the realities of contemporary higher education.

Course content

1. Student as Prosumer
2. Teaching as Selling Process
3. Prior Knowledge and Learning Outcomes
4. Group Projects and Peer Review
5. Nano Competencies
6. Course Design – Various Approaches
7. Measures and Course Improvement

General objectives

This course equips higher education instructors with modern pedagogical tools to enhance engagement, personalization, and course effectiveness. Participants will explore how to combine communication theory and business logic to create adaptive, student-centered learning environments.

Learning outcomes

By the end of this course, participants will be able to:

1. Design course outlines that align prior knowledge and student needs with intended learning outcomes, both for planned and adaptive learning paths.
2. Facilitate learning processes that leverage group diversity, including Belbin’s team roles and individual experience profiles.
3. Apply the Teaching as Selling approach to improve motivation, participation, and knowledge retention.
4. Define and develop nano-competencies as measurable micro-learning elements within a course.
5. Integrate peer review and feedback loops to enhance both teaching quality and learner autonomy.
6. Use data-driven approaches and evaluation metrics to continuously improve course effectiveness and student satisfaction.

Ewa Ura-Bińczyk, Warsaw University of Technology

Course description

Engineering education is at a crossroads. As industries demand graduates who can navigate complexity, uncertainty, and interdisciplinarity, higher education must go beyond transmitting technical knowledge. Research in the scholarship of teaching and learning (SoTL) shows that active, student-centered pedagogies significantly improve engagement, retention, and professional readiness. Yet, many engineering instructors still rely on traditional, lecture-centered models that underprepare students for real-world practice.

Course content

1. Reframing Teaching in Engineering
2. Backward Design
3. Active Learning
4. Assessment and Feedback
5. Rapport and Inclusivity
6. Professional Identity as a Teacher

General objective

This course equips higher education instructors - especially in engineering and technical disciplines - with a framework and toolkit for designing transformative learning experiences. Grounded in backward design and evidence-based pedagogy, the course explores how to integrate authentic industry cases, active learning strategies, and inclusive practices into the curriculum. Participants leave with a portfolio of teaching materials aligned with both disciplinary standards and pedagogical excellence.

Learning outcomes

By the end of this course, participants will be able to:

1. Apply backward design to align outcomes, learning activities, and assessment in engineering contexts.
2. Incorporate active learning methods (e.g., case-based learning, problem-based learning, simulations) into technical courses.
3. Design authentic assessments that mirror professional engineering practice and foster transferable skills.
4. Facilitate inclusive and equitable learning environments.
5. Critically reflect on their teaching philosophy and position themselves within the broader field of SoTL.

Marta Skierniewska, Warsaw University of Technology

Course description

Many universities continue to face equity gaps in participation and attainment among students from under-represented groups (e.g., first-generation students, migrants, disabled students, working/carer students, minorities). Barriers include the hidden curriculum, microaggressions, language and digital barriers, and inaccessible teaching and assessment formats. This course shows how to design supportive learning environments through the lens of equality, diversity and inclusion (EDI), aligned with CHET priorities, and applicable to hybrid and AI-enabled settings.

Course content

1. EDI Frameworks and Diagnosing Inequalities Intersectionality, hidden curriculum, microaggressions; rapid climate audit; working with LMS data (participation, log-ins, submissions) in a disaggregated view.
2. UDL and Accessible Materials Multiple means of engagement/representation/action; WCAG in practice (contrast, structure, alternatives); AI for accessibility: captions, alt-text, summaries, language support.
3. Inclusive Climate and Relationships Class contract and norms, moderating discussions, responding to microaggressions (micro-interventions), trauma-informed elements; low-barrier strategies for shy/new students.
4. Equitable Assessment and Feedback Authentic tasks, format options (text/video/diagram), transparent criteria; AI-assisted feedback with ethical safeguards; bias reduction (calibration, anonymity).
5. Hybrid Delivery and Digital Inclusion HyFlex/asynchronous pathways, low-bandwidth alternatives, OER; minimal equipment expectations; privacy and consent when using AI/digital tools.
6. Partnership with Students and EDI Evaluation Co-design with representatives of under-represented groups; signposting map of services; implementation plan and outcome indicators (participation, pass, satisfaction) with an iterative improvement cycle.

Learning outcomes

By the end of the course, participants will be able to:

1. Diagnose EDI barriers and gaps in their module using a short climate audit and disaggregated data (participation, attendance, pass rates).
2. Apply UDL (Universal Design for Learning) and WCAG by redesigning at least one activity and one resource to improve accessibility and provide multiple routes for participation.
3. Implement routines that build belonging and psychological safety (class contract, discussion norms, roles, check-ins).
4. Design equitable assessment: transparent rubrics, authentic tasks, options for demonstrating achievement, plus elements of anonymisation and bias reduction.
5. Use AI responsibly to support accessibility and feedback (captions, transcripts, summaries, language support), with clear appraisal of risks (bias, privacy, academic integrity).
6. Develop a support and referral plan (signposting to wellbeing, disability and financial services) and monitor progress using 3–4 EDI indicators.

Lourdes Canós-Darós and Oksana Polyakova Nesterenko, Universitat Politècnica de València

Course description

Mentorship in university is essential at all education levels - bachelor's, master's, and doctoral studies. It provides students with academic and professional guidance, helping them make informed decisions about their studies, select suitable courses, and prepare for the job market. Mentorship also offers emotional and motivational support, enabling students to face personal and academic challenges with greater confidence. Mentors serve as role models, encouraging the development of critical thinking, professional ethics, and leadership skills. They assist students in integrating into university life, particularly during their early years, by guiding them toward valuable resources and extracurricular opportunities. Lastly, mentorship offers a long-term perspective, allowing students to set clear goals and develop strategies to achieve them, thereby enriching their overall university experience.

Course content

UNIT 1: Foundations of academic mentoring

1. What is mentoring? The role of the university mentor: responsibilities, boundaries, and expectations
2. Models of mentoring: traditional, peer mentoring, academic coaching
3. Skills of an effective mentor: communication, empathy, active listening and leadership
4. New challenges in mentoring: AI, plagiarism and authorship

UNIT 2: Mentoring in university classrooms and online

1. Strategies for supporting student learning and engagement
2. Group vs. individual mentoring in class settings
3. Online mentoring scenario: tools and platforms
4. Feedback strategies and best practices

UNIT 3: Supervising Bachelor`s or Undergraduate Final Projects and Master’s Theses

1. Guiding topic selection, research questions, and methodology
2. Supporting time management and project planning
3. Reviewing written work and preparing for oral defence
4. Common challenges and conflict resolution

UNIT 4: Supervising Doctoral Theses

1. Navigating the doctoral journey: research stages and challenges
2. Building effective supervisor–candidate relationships
3. Fostering research autonomy, critical thinking and academic growth
4. Well-being and mental health: impostor syndrome, burnout and isolation

UNIT 5: Institutional Mentoring Programs and Strategies

1. The role of the university in fostering a mentoring culture
2. Designing and implementing institutional mentoring programs (for students, early-career faculty, researchers)
3. Case study: Student Support Plan "PIAE+" at the Universitat Politècnica de València
4. Best practices

General objective

By the end of this course, university faculty will be able to understand, apply, and critically reflect on academic mentoring practices in diverse university contexts.

Learning outcomes

1. Define academic mentoring and explain its significance in higher education.
2. Compare different mentoring models (traditional, peer mentoring, academic coaching) and evaluate their relevance in various academic settings.
3. Develop core mentoring skills such as effective communication, empathy, active listening, and leadership.
4. Reflect on emerging challenges in academic mentoring, including the implications of artificial intelligence, plagiarism, and authorship ethics.
5. Design actions that foster student commitment and academic perseverance.
6. Differentiate between group and individual mentoring approaches within classroom environments.
7. Utilize appropriate digital tools and platforms for effective online mentoring.
8. Guide students in defining, developing, and completing academic or applied research projects.
9. Fulfill the role of doctoral mentor from both academic and professional perspectives.
10. Support the well-being and mental health of doctoral students by fostering open communication, recognizing signs of distress, and promoting a balanced research journey.
11. Analyze the role of institutional mentoring programs in enhancing educational quality and student success.

Event details

Dates:

• Mon, 17.11.2025, 9:00–12:00 (online meeting)
• Wed, 19.11.2025, 9:00–12:00 (online meeting)
• Fri, 21.11.2025, 9:00–12:00 (online meeting)
• Tue, 25.11.2025, 10:00–11:00 (mentoring session, online)
• Tue, 25.11.2025, 16:00–17:00 (mentoring session, online)
• Fri, 28.11.2025, 9:30–11:30 (final presentation, online)

Scope:

• 13 hours of live meetings
• 12 hours of participant work

Format: Online (MS Teams)

Click here to apply

Course description

The complexity of present and future grand challenges require students to be educated differently, and to be taught different skills that embrace transdisciplinarity. However, understanding of transdisciplinarity and its subsequent translation into course design & competencies is oftentimes still lacking across faculties, let alone universities. If we want to be able to prepare students for a bright future, and equip them with the knowledge and competencies needed to solve current and future urgencies - we must integrate transdisciplinary approaches into teaching.

Course content

This course will therefore focus on transdisciplinarity in education, and break this down into three different aspects: (1) TD as a worldview/approach, (2) TD as a competency, and (3) TD as a teaching mode/practice.

Those who would follow the course would gain a clear understanding of what we mean when we consider transdisciplinarity, and its scope (narrow <-> broad, deep v superficial), and how this might differ slightly depending on the context of research or education. Aside from defining the concept, participants will also learn why transdisciplinarity is essential to future-proofing education - and particularly engineering education at that. This includes outlining how different versions or elements of transdisciplinarity may apply or be relevant in enriching educators' current approach to education and teaching, and touch upon related topics such as experiential learning, community-engaged learning and e.g. civic engagement as an institutional value.

Moreover, participants would walk away from the module with an overview of the different skills that students develop as a result of transdisciplinary education - as well as considering constructive alignment and potential entry requirements in order to do well in transdisciplinary settings. The skills discussed should be understood as essential, critical academic skills and include the importance of positionality and power in science/research as well.

Here, we also zoom in on the various tools that may facilitate transdisciplinarity in education. Identifying the skills that students need in order to partake in transdisciplinary education or succeed in transdisciplinary projects - but how do we deliver such education or design such projects? Participants will leave with tips and tricks on recurring elements to build sessions and activities that have transdisciplinarity at the core.

Lastly, we will focus on transdisciplinary activities and assessment. How can transdisciplinarity be integrated into course design, and how can transdisciplinarity also be assessed, building on e.g. the skills they've identified so far? Participants will familiarise themselves with existing frameworks that can be used to create more robust rubrics and dispel the framing of transdisciplinary skills as 'soft' or 'subjective' skills only. Moreover, we will explore non-traditional assessment methods that include artifacts such as the usage of photo- and videography, podcasts, etcetera as valid knowledge productions.

Elżbieta Karwowska, Piotr Majewski, Tomasz Balicki, Elena Atanasiu, Gdańsk University of Technology

Course description

This course addresses the challenges and opportunities of neurodiversity in education and the workplace. In higher education, neurodivergent students and staff often face systemic barriers, exclusion, or outdated support strategies. At the same time, inclusive approaches to neurodiversity have been shown to foster innovation, psychological safety, and belonging. The course is particularly relevant to the CHET program as it combines research-based insights, practical methods, and participatory exercises to equip educators with tools for building inclusive and adaptive learning environments.

Course content

1. Neurochallenge – Experiencing and analysing the challenges of neurodivergent individuals in academia and the workplace; diagnosing issues in higher education contexts.
2. Perceptions of Neurodiversity Over Time – From deficit models to social models and visions for the future; needs of neurodivergent individuals.
3. Inclusive Environments I – Exploring positive and problematic framings of neurodiversity; lessons from Teal organisations.
4. Inclusive Environments II – Universal Design for Learning; case studies, role play, and team-based redesign of workflows.
5. Neurodiversity & Psychological Safety – From inclusion to innovation through the four stages of psychological safety: inclusion, learner, contributor, challenger.

General objective

The overall objective of the course “Neurodiversity in Education” is to enable educators and academic professionals to understand, value, and effectively support neurodiversity within educational and workplace settings. Through research-based insights, reflective practice, and participatory learning, participants will develop the knowledge, empathy, and practical skills needed to design inclusive environments that promote psychological safety, autonomy, and belonging for neurodivergent individuals. The course aims to transform traditional approaches to teaching and collaboration by integrating Universal Design for Learning (UDL) and Teal organisational principles, fostering innovation, equity, and human-centered learning cultures in higher education.

Learning outcomes

1. Recognize key challenges faced by neurodivergent individuals in academia and the workplace.
2. Analyse and diagnose exclusionary practices using structured reflection tools.
3. Compare historical and current perceptions of neurodiversity, and imagine future inclusive approaches.
4. Apply Universal Design for Learning (UDL) and Teal organisational practices to foster autonomy, belonging, and participation.
5. Create inclusive strategies that enhance psychological safety and innovation.
6. Design and present practical solutions for fostering neurodiversity at campus level.

Andrzej Manujło, Warsaw University of Technology

Course description

A hands-on course in which participants learn to turn their students’ ideas into working prototypes by combining Arduino, basic electronics, introductory programming, Processing (MIT Media Lab), and rapid fabrication (3D printing, laser cutting, and other maker-lab methods) with AI for ideation, design support, and API-level integration. The course is practitioner-led and draws on the instructor’s field-tested curricula for university students and PhD candidates; a 2020 Ministry of Education award for co-creating the innovative Rapid Engineering Prototyping course; and experience training 1,000+ teachers in the national IT Mastery Center (CMI) program.

General objective

Higher education faces a skills gap between theory and making: students can analyze but struggle to prototype quickly, iterate with users, and leverage AI beyond chat interfaces. Programs need scalable, hybrid-ready courses that embed project-based learning (PBL), design thinking, and gamification while aligning with digital skills frameworks. This course addresses that gap by uniting physical computing and AI tooling so learners can progress from a blank page to a tested MVP in weeks - not semesters.

Learning outcomes

By the end of the course, participants (teachers) will be able to design and run classes where students:

1. Apply design thinking to frame problems, define users, and craft testable value propositions.
2. Generate and refine project ideas with AI (prompting, brainstorming canvases, critique loops).
3. Build simple electronic circuits (sensors/actuators) and read schematics.
4. Program Arduino and Processing to acquire sensor data, control outputs, and visualize interactions.
5. Model printable parts in 3D CAD and prepare them for 3D printing; design for laser cutting.
6. Integrate an AI API (e.g., LLM/vision/speech) into a prototype.
7. Implement and explain basics of AI agents (tool use, planning loops, prompt/state management) for simple tasks.
8. Work in blended learning sprints with checkpoints, peer reviews, and gamified progress.
9. Conduct quick user tests, collect evidence, and iterate to improve usability and functionality.
10. Present a demo and defend design choices with metrics and next-step roadmaps.

Course description

The energy sector is undergoing rapid digital transformation, with mathematical modeling and artificial intelligence (AI) playing a crucial role in designing efficient, sustainable, and innovative solutions. Engineers and researchers are increasingly expected to understand how advanced modeling tools and AI techniques are applied in real-world projects, even when they do not directly operate specialized software.

This course introduces participants to the principles, methodologies, and applications of mathematical modeling and AI in energy systems, using examples from industry-standard tools such as Aspen HYSYS, GateCycle, SIPEP, and Ebsilon Professional. Delivered fully online, the course focuses on conceptual understanding, analysis of case studies, and interpretation of modeling results, preparing students to engage with digital tools and apply AI-supported decision-making in higher education and professional practice.

Course content

Part 1: Introduction to Energy Systems Modeling

• Why modeling matters in energy engineering and policy.
• Digitalization and AI as key enablers in higher education and industry.
• Overview of common modeling approaches and tools (illustrative examples).

Part 2: Process Simulation Concepts

• Principles of process modeling and thermodynamics.
• Applications in gas processing, hydrogen production, and CO₂ capture.
• Demonstration of how AI enhances optimization and control.

Part 3: Power Plant Performance Analysis

• Thermodynamic cycle concepts (Rankine, Brayton, combined cycles).
• How digital tools simulate plant performance under different conditions.
• Case studies: efficiency improvements and renewable integration.

Part 4: Energy Planning and Scenario Analysis

• Modeling at the system and policy level.
• Building scenarios for renewable transition. 
• Using AI for forecasting and scenario comparison.

Part 5: Performance Monitoring and Digital Twins

• Concept of digital twins in energy systems.
• Predictive maintenance and fault detection.
• Case studies of hybrid and renewable-based power plants.

Part 6: Integration and Future Trends

• Linking modeling concepts with AI and machine learning.
• Role of digital tools in decarbonization and sustainable energy.
• Reflection: opportunities and challenges for higher education.

General objective

To provide participants with a strong foundation in digital modeling approaches for energy systems, and to demonstrate how AI can enhance analysis, optimization, and decision-making in complex projects.

Learning outcomes

By the end of the course, participants will be able to:

1. Explain the role of mathematical modeling in energy system design, analysis, and policy.
2. Interpret simulation results from process and power plant modeling tools.
3. Understand how AI and data-driven methods support optimization, forecasting, and predictive maintenance.
4. Evaluate case studies of energy projects that integrate modeling and AI.
5. Discuss digitalization trends and their relevance for higher education and sustainable energy.

Course description

This course explores practical strategies for integrating AI into both the design and assessment of educational processes. In the first part, you’ll harness AI tools to create engaging learning experiences that develop higher order skills - using an updated Bloom’s taxonomy, configuring GPT models with strategies like scaffolding, discussions, debates, and simulations, and incorporating Universal Design for Learning (UDL) to promote self-directed study. In the second part, you’ll examine modern approaches to assessing learning outcomes that move beyond traditional grading to supportive, competency-based evaluation, addressing the unique needs of today’s digital generation. Designed for educators eager to innovate their teaching and assessment practices, this course equips you with actionable insights and practical tools.

Course content

This course is organized in two modules:

1. Designing AI-enhanced educational processes
2. Innovative approaches to assessing learning outcomes

By the end of this course, you will be equipped with the knowledge and strategies to design and assess AI-driven educational processes that foster higher order thinking and create supportive, future-ready learning environments.

General objective

To empower educators with the knowledge, skills, and tools to design, implement, and assess AI-enhanced learning experiences that promote higher-order thinking, learner autonomy, and inclusive, competency-based education.

Learning objectives

By the end of this course, participants will be able to:

1. Analyze how AI technologies can enhance the design of educational processes in alignment with Bloom’s taxonomy and Universal Design for Learning (UDL) principles.
2. Apply AI tools (e.g., GPT-based models) to create interactive and engaging learning activities, such as debates, simulations, and scaffolded discussions.
3. Design AI-enhanced learning experiences that support diverse learners, encourage self-directed study, and foster creativity and critical thinking.
4. Develop assessment strategies that move beyond traditional grading toward formative, competency-based, and learner-centered evaluation.
5. Evaluate the ethical, pedagogical, and practical implications of integrating AI in teaching and assessment practices.
6. Create a prototype of an AI-supported educational activity or assessment aligned with learning outcomes and inclusive design principles.