TEACHING
EXCELLENCE
DATABASE

Artificial Intelligence (AI) is increasingly shaping vocational education by impacting job profiles across various fields, emphasizing skills like technical, social, and emotional competencies while decreasing the demand for manual tasks. The rapid development of text-generative AI tools since late 2022 has raised challenges in educational institutions. In response, a team of educators designed three AI-focused teaching concepts for the 2023/24 academic year aimed at preparing vocational education students to become AI-competent teachers. These students are likely to teach pupils affected by AI-induced job shifts in fields like office management, applied computer science, media informatics, and IT business solutions. The three teaching concepts—Prompt Engineering, Fact-Check, and AI Cooperation—are grounded in media and information literacy, enhancing critical skills necessary for assessing, using, and creating content responsibly. This framework is informed by Bloom's taxonomy and Biggs' constructive alignment, aligning learning goals with activities and evaluations 1 Prompt Engineering introduces students to prompt engineering basics, enabling them to create prompts that yield high-quality AI responses. After initial guidance, students engage in hands-on exercises, crafting prompts for specific tasks like sales correspondence. AI-generated results are evaluated based on criteria such as relevance and coherence, with group reflections on optimization strategies. Ultimately, students select the most effective prompt based on collective analysis. 2 The Fact-Check concept, students critically assess AI-generated information against specialized literature, particularly on topics like Direct Instruction methods. They document discrepancies, discussing them in a plenary setting with a focus on AI’s credibility. This process fosters a nuanced understanding of AI’s role in scientific contexts, underscoring its strengths and limitations in knowledge transfer and validation. 3 AI Cooperation emphasizes collaboration with AI tools in group projects, such as analyzing e-commerce trends or creating pedagogical guidelines. While students are encouraged to use AI, its contributions must be clearly marked in the final output. Educators observe group interactions in virtual settings, documenting collaboration phases and identifying patterns in AI usage. During presentations, students critically reflect on the advantages and limitations of AI in collaborative work.

Methodology

The integration of AI-focused teaching concepts in vocational education followed a structured process to foster students' critical engagement with AI tools and enhance their digital skills. This approach was divided into phases, encompassing detailed planning, effective implementation, thorough review, and collaborative partnerships, ensuring a comprehensive and adaptable teaching framework. In the planning phase, the process began with a needs assessment to identify essential skills for an AI-driven job market, emphasizing analysis, synthesis, and evaluation in alignment with Bloom’s taxonomy. The teaching team developed three modular concepts—Prompt Engineering, Fact-Check, and AI Cooperation—designed to integrate flexibly across various courses. Each concept followed a structured progression from introductory sessions on AI and media literacy to practical tasks, documentation, and critical reflection, helping students move from theoretical understanding to practical application. The implementation phase was carefully structured, with each concept introduced sequentially to allow a gradual skill buildup. Prompt Engineering helped students create effective prompts for AI tools, Fact-Check emphasized critical evaluation of AI-generated content against credible sources, and AI Cooperation encouraged collaborative projects where groups used AI to support research and presentations. Educators facilitated the process, while students engaged actively in peer-to-peer learning and collaborative problem-solving, building confidence in using AI tools effectively and critically. In the review phase, feedback was essential for assessing each concept’s effectiveness and identifying areas for improvement. After each course, educators collected feedback from students through surveys and reflective sessions, evaluating student engagement, comprehension, and skill development. This feedback loop enabled continuous improvement, with adjustments made to address challenges, such as adding resources for prompt formulation and content evaluation. The iterative review process aligned each concept more closely with student needs, ensuring that each iteration became more effective and relevant. Collaboration among partners was critical for successful implementation. Educators facilitated student learning, while students took an active role, providing feedback that informed necessary adjustments. Institutional stakeholders provided resources and ensured alignment with broader educational goals. Additionally, collaboration with curriculum advisors and educational technologists enriched the process, bringing in best-practice insights and supporting the development of resources tailored to vocational education. The approach fostered a collaborative learning environment that encouraged both educators and students to share insights, fostering a culture of continuous improvement and shared ownership. Student feedback was highly valued, leading to iterative refinements that enhanced the concepts and promoted innovation in teaching. Through reflective practice, students came to see AI as a tool that transforms task approaches rather than merely replacing human effort, enhancing critical thinking and collaborative skills. This understanding helped students shift their perception of AI’s role, reinforcing a reflective and adaptive learning environment that emphasized peer learning and active participation. Overall, the structured integration of these AI-focused teaching concepts not only addressed immediate educational needs but also established a sustainable model for incorporating AI in vocational education. This approach provides a lasting impact, equipping students with essential skills for an evolving digital landscape. Within 7 stepts the concepts were explained to the students to provide a didactical framework for their own teaching: 1. Define the Target Group 2. Establish Context 3. Specify Concrete Content (Learning Objectives) 4. Create Instructions for Learners 5. Observe: Monitor learners as they work with AI 6. Reflect with Learners and Define Improvement Potential 7. Evaluate and Improve

Tools, equipment, technology used

Main tools are the three teaching concepts: Teaching concept 1 - PROMPT-ENGINEERING The aim of the teaching concept is to familiarise students with the basics of prompt engineering and to enable them to independently develop prompts that lead to high-quality results with text-generative AI. To begin with, students are introduced to the concept of prompt engineering. They then focus on practical exercises in which prompts are created for specific use cases, such as correspondence in the course of concluding a sales contract. The results generated by the AI are continuously evaluated based on criteria such as relevance, coherence, plausibility and correctness. This evaluation serves to gradually optimise the prompts and achieve an optimal result, for example when creating an offer in a given business scenario. Reflection on the process, including all the prompts created and the AI results, takes place in the plenary session in order to finally evaluate the criteria mentioned and to reflect on and discuss the effectiveness of this AI-supported method. At the end, the student group jointly selects the prompt that has achieved the best result. Teaching concept 2 - FACT-CHECK The teaching concept Fact-check aims to promote a critical examination of the results of text-generative AI, particularly in the context of specific subject content such as the description of the methods of Direct Instruction. Students learn to systematically compare the content generated by the AI with relevant specialised literature in order to identify discrepancies and similarities. An essential component is the careful documentation of the differences between the AI results and the information from the specialised literature. This analysis is presented and discussed in the plenary session, with a particular focus on the credibility of the AI-generated information. Reflecting on the results found enables a deeper examination of the question of the extent to which AI can serve as an aid in scientific contexts or possibly provide misleading or false facts. The aim is to develop a critical understanding of the possibilities and limitations of text-generative AI in knowledge transfer and verification. Teaching concept 3 - AI-COOPERATION The AI-Cooperation teaching concept focuses on cooperation with AI tools and promotes the integration of AI in the processing of scientific topics and related study assignments in the context of group work, such as the analysis of e-commerce and m-commerce in Austria or the development of pedagogical guidelines for a fictitious school. Students are provided with both scientific resources and AI-supported tools. While the use of AI is expressly encouraged, its use in the final result must be clearly labelled. The results of the group work are presented in the form of a presentation and assessed by the teacher. A special feature of this concept is the observation of the group during the development phase (ideally carried out in a virtual room - teachers are in the same virtual room without sound and images and after explicit description of the observation project with the knowledge of the students). An observation protocol, which is created by the teachers, records the results of the course of the work phase. As part of the presentation, the students critically analyse the advantages and challenges of AI collaboration. Based on the teachers' observations, it can be determined, for example, that in all cases AI was only used to summarise the specialist literature.

Outcomes and outputs, main results

The implementation of AI-focused teaching concepts in vocational education produced substantial results, demonstrating the effectiveness of this innovative approach. Students showed significant progress in AI competencies, particularly in prompt engineering, critical evaluation of AI-generated content, and collaboration with AI tools. Pre- and post-feedbacks revealed increased confidence and proficiency, as students learned to create precise prompts, assess AI reliability, and apply these skills in group projects. This structured approach enhanced their critical media literacy by encouraging comparisons between AI-generated and academic content, deepening their understanding of ethical and legal implications associated with AI use. The modular teaching framework—divided into phases of introduction, task execution, documentation, and reflection—proved successful in guiding students through a logical progression of AI skills. This structure has become a valuable resource that can be adapted by other educators across various courses, making it a scalable model for teaching AI literacy. The approach also cultivated a reflective learning environment, as students actively engaged in group discussions, peer reviews, and iterative improvements of their work, reinforcing teamwork and critical thinking skills. Through a systematic review phase, the teaching team gathered valuable feedback, creating a continuous improvement loop. Adjustments were made based on student input, such as adding support materials for prompt design and content evaluation, which refined the concepts further. This feedback-driven process not only improved teaching quality but also established a sustainable model for integrating AI in vocational education. The project’s data provided insights into AI literacy development, supporting the institution’s strategic goals of embedding digital competencies into vocational education. This approach not only strengthens digital literacy and critical engagement with technology but also equips students with essential skills for the challenges of an evolving workplace.

Lessons learnt

The structured integration of AI-focused teaching concepts in vocational education effectively prepared students for the challenges of a digitalized knowledge society. By combining theoretical knowledge with hands-on application and critical reflection, these concepts promoted a balanced understanding of AI as a valuable, yet nuanced, tool in educational and professional contexts. Through collaborative planning, implementation, and review, the teaching team created a sustainable model that could be adapted across courses and educational levels. This approach not only met institutional and policy goals but also supported a future-oriented learning culture that encourages continuous development and adaptation in an evolving digital landscape.

Adaptability and sustainability of the best practice (for other institutions)

The three teaching concepts are highly adaptable across various courses and disciplines, particularly in interactive formats like seminars and exercises, where they support active student participation and graded outputs. They foster deep engagement with content, critical thinking, and practical application. Elements of "Prompt Engineering" and "Fact-Check" can also enrich lectures as innovative assessment formats, such as analyzing AI-generated content or creating custom prompts as part of the evaluation. Regardless of the format, these concepts consistently promote a reflective use of AI and strengthen critical thinking. As universal frameworks, they integrate text-generative AI into higher education, enhancing media literacy, critical thinking, and practical skills across academic contexts.

Promotion of best practice

To promote our AI-focused teaching concepts, we engaged in multiple dissemination activities. At the “Tag der Lehre,” we presented to the entire faculty, explaining the concepts’ impact on critical thinking and digital skills, sparking interest across departments. A radio interview allowed us to reach a broader audience, emphasizing AI’s role in education. Two press interviews highlighted the concepts’ contribution to preparing students for a digitalized workforce, positioning our institution as an innovator. Regular “DIGI ESPRESSO” sessions provided an informal space for peer exchange, where colleagues discussed practical insights and applications. Presenting to the leadership team secured strategic support, aligning the concepts with institutional goals. Lastly, at the vocational education conference in February 2025, we will share our best practices with a wider network of educators and policymakers, encouraging broader adoption. Through these targeted activities, we aim to ensure lasting impact and foster collaboration in AI-enhanced education.

Scope and impact

• Course/department level
• Faculty level
• Institutional level
• Cross-institutional level
• National level
• EU/EHEA/International level

The AI-focused teaching concepts address educational challenges and contribute to strategic goals across multiple levels. At the course and department level, they enhance quality by promoting critical engagement with AI tools, aligning with learning objectives, and developing skills like critical thinking and media literacy essential for vocational education. This targeted approach equips students with practical AI competencies. At the faculty level, these concepts model the integration of AI and digital literacy in teaching, fostering a culture that values adaptability. Institutionally, they support strategic aims to prepare students for a tech-driven workforce, boosting employability and enhancing the institution's standing in digital education. The cross-institutional impact is facilitated by their modular design, allowing other institutions to adopt them flexibly. Nationally, these concepts help vocational education goals by building digital literacy, preparing students for an AI-driven job market. At the EU/EHEA level, they align with objectives to enhance digital skills and lifelong learning, contributing to the EU’s mission of developing digitally skilled, employable citizens ready for a rapidly evolving workforce.

6.1 Digitalization

• Innovative, novel methodology in using digital tools/devices in teaching
• Digital skills development and assessment both general and profession-related, embedded in course design, in teaching and assessment
• Artificial intelligence and learning analytics in education and training

Reasoning: Our AI-focused teaching concepts emphasize innovative digitalization, fostering AI literacy, digital competencies, and critical thinking. Content Delivery and Accessibility were ensured via an LMS, allowing students to access resources and AI tools remotely, making complex digital skills accessible. Active Engagement was promoted through hands-on AI exercises, such as prompt creation and fact-checking, with peer reviews fostering collaborative learning. Digital Competence Development was prioritized, with students learning ethical AI use by evaluating AI-generated content and discussing issues like bias and data privacy. Assessment and Feedback were facilitated digitally, offering real-time feedback on tasks through the LMS, enabling iterative improvement. Overall, these digital tools and methods created a comprehensive, responsible learning environment that prepares students for AI-driven challenges.

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6.2 Internationalization

• NOT RELEVANT

Reasoning:

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6.3 Inclusion and diversity, universal design

• NOT RELEVANT

Reasoning:

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6.4 Sustainability

• NOT RELEVANT

Reasoning:

3.3 Public contact datas

Name	Email address	Website
Patrick Pallhuber	patrick.pallhuber@ph-tirol.ac.at	https://ph-tirol.ac.at/ars-docendi-anerkennungspreis-exzellente-ki-lehre-ph-tirol