Prof. Dr. Rıdvan ELMAS
Biyografi
Prof. Dr. Rıdvan Elmas is a faculty member at the Atatürk Faculty of Education at Marmara University. He completed his doctoral degree at Middle East Technical University and has conducted academic work at Utrecht University, Purdue University, and Charles University Prague. His scholarly expertise spans STEM education, sustainability, systems thinking, context-based learning, and curriculum studies. In recent years, he has focused on the integration of artificial intelligence into education, AI literacy, generative AI-supported learning design, and teacher education. His current research explores how science education, teacher competencies, and future learning models can be reimagined in the age of artificial intelligence.
Artificial Intelligence Ecosystem in Science Education: A Hands-on Design Workshop with Intelligent Assistants
Workshop Objectives
The aim of this workshop is to introduce science teachers, science education researchers, and pre-service teachers to how artificial intelligence (AI)-supported technologies can be used effectively, consciously, ethically, and pedagogically meaningfully in science learning processes. Within the scope of the workshop, participants are expected to gain hands-on experience with the opportunities offered by AI in areas such as content management, generating high-quality instructional inputs, designing learning materials and presentations, preparing lesson plans, developing activities, and supporting scientific inquiry processes.
The workshop will address various AI tools not only in terms of their technical features, but also in terms of how they can be transformed into learning designs that support students’ active participation, scientific thinking, questioning, data interpretation, and evidence-based explanation development. In this context, participants will discuss the importance of considering contextual elements such as grade level, learning objectives, student profiles, misconceptions, activity types, assessment methods, scientific accuracy, and ethical use principles in order to obtain high-quality learning outputs from AI systems. Furthermore, the workshop will provide an opportunity to evaluate AI as a responsible pedagogical tool that enhances science lessons by making them more visual, interactive, context-based, reliable, and learner-centered.
Target Audience
- Science teachers
- Academics and researchers in science education
- Pre-service science teachers
- Educators interested in developing AI-supported lesson plans, activities, and learning materials
Expected Learning Outcomes
By the end of the workshop, participants are expected to be able to:
- Analyze the pedagogical value of AI-supported tools in science learning processes;
- Develop original AI-supported science learning designs by considering grade level, learning objectives, student profiles, misconceptions, scientific accuracy, and ethical use principles;
- Critically evaluate the quality of AI-generated science learning outputs.
Duration and Format
Total Duration: 90 minutes
Format: Brief theoretical framework, hands-on tool experience, design-based group work, sharing and evaluation
The workshop will be conducted in a practical format in which participants are not merely listeners but active designers. The process will include stages such as conceptually addressing the pedagogical use of AI-supported tools in science learning, experiencing sample applications, developing learning materials, and critically evaluating the developed products.
- Conceptual Framework and Pedagogical Positioning
The potential of AI in science learning processes will be examined in terms of supporting content management, generating high-quality instructional inputs, developing learning materials, designing presentations, creating activities, and facilitating scientific inquiry processes. In this section, AI will be discussed not merely as a technical tool, but as a pedagogical opportunity that supports learning design, student engagement, and scientific thinking.
- Hands-on Tool Experience and Case Analysis
Participants will engage with selected AI-supported application scenarios to experience processes such as content organization, generating instructional inputs, designing presentations and visual materials, preparing lesson plans, and developing activities. The outputs generated by these tools will be evaluated collaboratively in terms of scientific accuracy, pedagogical appropriateness, alignment with learner level, and ethical use.
- Design Workshop: Developing Science Learning Materials
Participants will work individually or in small groups to develop an AI-supported learning design aligned with a specific science topic or learning objective. This design may include one or more of the following: lesson plans, activities, presentations, visual materials, scientific inquiry tasks, or formative assessment components. Participants are expected to produce a comprehensive output by considering grade level, student profiles, misconceptions, learning objectives, scientific accuracy, and ethical use principles.
- Sharing, Critical Evaluation, and Closing
Participants will present their developed examples, which will be discussed collectively in terms of scientific accuracy, pedagogical quality, student engagement, inquiry skills, applicability, ethical use, and depth of learning. At the end of the workshop, participant feedback will be collected, and recommendations regarding the responsible, creative, and sustainable use of AI in science education will be evaluated.