Teaching in the Age of AI

 

Generative AI has quickly become part of the teaching landscape. Educators are using it to prepare lesson materials, design assessments, generate examples, and create practice activities that reinforce student learning. At the same time, many are asking how these tools might influence their professional judgment and their relationships with students. Like earlier shifts in educational technology, AI is prompting educators to reflect on what aspects of their practice are essential and what might be strengthened through new tools.

History offers perspective. Each wave of technology, from calculators to online platforms, has raised concerns and created opportunities. Over time, these tools found their place in classrooms when teachers discovered how to use them to support learning rather than distract from it. Generative AI is the latest of these shifts. The question for educators today is not whether AI belongs in teaching, but how to design instruction that uses it purposefully while keeping pedagogy first.
 

Looking Back
In our earlier Generative AI Meets Pedagogy series, we examined how AI can connect with three teaching frameworks: Universal Design for Learning, Active Learning, and Experiential Learning. Each article highlighted ways that AI might extend existing approaches when guided by clear pedagogy. This mirrors what researchers are now finding. A recent review of generative AI in K–12 settings concluded that the most promising uses occur when educators align AI tools with specific instructional goals and receive adequate preparation to use them effectively (Marzano, 2025). When aligned with strong pedagogy, AI can support personalized learning, strengthen student motivation, and provide timely feedback.

In higher education, similar themes are emerging. Universities worldwide are experimenting with generative AI in teaching and learning, but challenges such as academic integrity and assessment design remain pressing. A recent study argues that the key question for universities is not whether AI will transform teaching, but how educators will reshape learning experiences to emphasize authenticity, critical thinking, and meaningful engagement (Yusuf, Pervin, & Román-González, 2024). Across K–12 and higher education, the message is clear: AI is most valuable when it is grounded in pedagogy rather than driving it.

Pedagogy First, Still
Teaching has always been more than the tools available. It is about relationships, curiosity, and the thoughtful design of learning experiences. Generative AI cannot replace those human elements, but it can create opportunities when used with purpose. Without direction, AI risks encouraging work that lacks depth or originality, or overreliance on quick answers. With careful integration, however, it can give educators more flexibility, support feedback, and allow more time for meaningful interaction with students.

Rather than asking what AI can do, the more important question is what educators want students to learn and experience. That clarity allows them to decide where AI might add value and where it should be limited. Keeping pedagogy first ensures that creativity, collaboration, and critical thinking remain at the heart of education. In this way, AI becomes not a replacement for teaching, but a support that allows educators to focus on what matters most: guiding students toward deeper understanding and meaningful learning.

What’s Ahead in This Series
With this foundation in mind, Teaching in the Age of AI will focus on the core practices that shape everyday instruction. Rather than centering on tools, each article will examine a key element of teaching and consider how it can be strengthened when AI is present. The goal is not to replace what educators already do well, but to highlight ways AI can be used thoughtfully to support teaching and learning.

Articles for this blog are published four times a year at the end of January, April, August, and December. Following today’s launch article, the series will continue with Assessment in the Age of AI in December 2025, Feedback in the Age of AI in January 2026, and Collaboration in the Age of AI in April 2026. Each article will return to a single principle: pedagogy comes first, and technology should serve as a tool to extend, not replace, good teaching.

Looking Ahead
We will begin this series with Assessment in the Age of AI. Assessment has always shaped how students approach learning—what they prioritize, how they spend their time, and what they value. With AI reshaping how students complete tasks, receive feedback, and create projects, assessment is the right place to start reimagining teaching in this new era. In the next article, we will consider how both formative and summative assessments can remain authentic, fair, and aligned with pedagogy when AI is part of the classroom.

References
Marzano, D. (2025). Generative artificial intelligence (GAI) in teaching and learning processes at the K-12 level: A systematic review. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09853-7

Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(21). https://doi.org/10.1186/s41239-024-00453-6

Generative AI Meets Experiential Learning

Our previous exploration examined how generative AI can enhance active learning by fostering engagement and critical thinking. Now, let's delve into its role in experiential learning, where students gain knowledge through direct experiences and reflection. Educators can create dynamic, reflective, and authentic learning experiences that prepare students for real-world challenges by integrating Generative AI (GenAI) into experiential learning. In this article, we will examine how generative AI can be integrated into each phase of Kolb’s Experiential Learning Cycle to bring new depth, adaptability, and responsiveness to experiential education (Salinas-Navarro et al., 2024).

What Is Experiential Learning?

Experiential learning is an educational approach where learners acquire knowledge and skills through direct experiences, followed by reflection and conceptualization. This method emphasizes active participation, allowing students to engage deeply with the subject matter.
Kolb, a prominent educational theorist, formalized this concept in his Experiential Learning Theory. According to Kolb (1984, 2014), effective learning occurs when a learner progresses through a four-stage cycle:

1. Concrete Experience: Engaging in a new experience or encountering a familiar situation in a novel way.
2. Reflective Observation: Thoughtfully reviewing and reflecting on the experience.
3. Abstract Conceptualization: Formulating theories or models based on reflections.
4. Active Experimentation: Applying the new ideas to the world around them to see what results.

This cyclical process allows learners to transform experiences into knowledge, fostering deeper understanding and the ability to apply learning in various contexts.

Experiential learning can take many forms, including internships, service-learning projects, simulations, and fieldwork. By immersing students in real-world challenges, this approach bridges the gap between theoretical knowledge and practical application, preparing learners for complex problem-solving and decision-making in their future careers.

Generative AI Integration

With the advent of Generative AI (GenAI), educators have new opportunities to enrich each stage of this cycle. By thoughtfully integrating GenAI tools, we can create more immersive experiences, facilitate deeper reflection, support conceptual understanding, and encourage innovative experimentation. This section explores how GenAI can be effectively embedded within each phase of Kolb's model to enhance experiential learning outcomes.

1. Concrete Experience: Engaging in Authentic Scenarios

In this initial stage, learners immerse themselves in new experiences, forming the foundation for subsequent reflection and learning. GenAI can facilitate this by generating realistic simulations and scenarios tailored to specific learning objectives. For instance, AI-driven role-playing exercises can place students in complex situations—such as managing a business crisis or conducting a scientific experiment—allowing them to engage actively and make decisions in a risk-free environment. These AI-generated experiences provide diverse contexts, enhancing learners' exposure to varied situations.

2. Reflective Observation: Facilitating Deep Reflection

Following the experience, learners reflect on their actions and outcomes to derive insights. GenAI tools can support this reflective process by prompting learners with thought-provoking questions like, "What challenges did you encounter?" or "How did your decisions impact the outcome?" Additionally, AI can assist in summarizing key events from the experience, helping learners to identify patterns and areas for improvement. This structured reflection fosters critical thinking and self-awareness.

3. Abstract Conceptualization: Developing Theoretical Understanding

In this phase, learners integrate their reflections to form abstract concepts and generalizations. GenAI can aid by connecting learners' experiences to relevant theories and frameworks, offering explanations and resources that deepen understanding. For example, after reflecting on a group project, AI can introduce concepts of team dynamics or leadership theories that relate to the observed outcomes. This integration bridges practical experiences with academic knowledge, solidifying learning.

4. Active Experimentation: Applying Knowledge in New Contexts

The final stage involves applying newly acquired knowledge to different situations, testing hypotheses, and observing results. GenAI can propose new scenarios or modify existing ones, allowing learners to apply their conceptual understanding in varied contexts. For instance, after learning about conflict resolution strategies, AI can simulate a new workplace dispute for the student to navigate, reinforcing and testing their skills. This iterative process promotes adaptability and continuous learning.

For guidance on writing effective prompts, refer to the Best Practices for Writing Effective Prompts.

Conclusion

Incorporating generative AI into Kolb’s Experiential Learning Cycle not only enriches each stage but also fosters a dynamic, iterative learning process. By leveraging GenAI tools, learners can engage in authentic experiences, receive immediate feedback, and apply newfound knowledge in varied contexts. This integration promotes deeper understanding, adaptability, and continuous improvement, aligning with the principles of authentic assessment and preparing students for real-world challenges.

References:

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.

Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson Education.

Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Designing experiential learning activities with Generative Artificial Intelligence tools for authentic assessment. Interactive Technology and Smart Education, 21(4), 708–734. https://doi.org/10.1108/itse-12-2023-0236
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Washington, G. (2025, April 30). Generative AI Meets Experiential Learning [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/
 

Generative AI Meets Active Learning

In our ongoing exploration of how generative AI intersects with pedagogical frameworks, we've previously discussed its alignment with Universal Design for Learning (UDL). Now, let's delve into how generative AI can enhance active learning—a dynamic approach that emphasizes student engagement through activities like problem-solving, discussion, and application of concepts. By thoughtfully integrating AI tools into active learning environments, educators can create more responsive, personalized, and effective learning experiences that cater to diverse student needs.

 What is Active Learning?

Active learning is a student-centered pedagogical approach that shifts the focus from passive reception of content to meaningful participation in the learning process. Rather than simply listening to lectures or memorizing facts, students are engaged in higher-order thinking activities such as analyzing, synthesizing, evaluating, and applying information through collaborative discussions, peer teaching, and problem-solving exercises. These activities foster deeper understanding and retention of knowledge by placing learners in the role of active constructors rather than passive consumers of information.

Incorporating active learning strategies into teaching not only promotes critical thinking and metacognitive skills but also increases student satisfaction and motivation (ElSayary, 2024). By being actively involved in the learning process, students are better equipped to understand complex concepts, make connections across disciplines, and apply knowledge in real-world contexts. These benefits are foundational for developing 21st-century competencies such as adaptability, collaboration, and lifelong learning—qualities that are increasingly essential in today’s dynamic educational landscape.

As education evolves in response to digital innovation, integrating emerging technologies like generative AI offers new opportunities to enhance active learning practices. The following section explores how generative AI can be thoughtfully integrated in active learning environments to deepen student engagement, support metacognitive regulation, and foster personalized learning.

 Integrating Generative AI into Active Learning

Generative AI tools, such as ChatGPT and Claude, can be powerful allies in promoting Active Learning. Their capabilities extend beyond mere information retrieval, offering dynamic interactions that foster deeper engagement and critical thinking among students.

​Enhancing Student Engagement and Critical Thinking

Generative AI tools can simulate Socratic dialogue, prompting students to think critically and articulate their understanding. These tools facilitate interactive learning experiences by enabling students to explore concepts through dialogue, question formulation, and iterative feedback. This approach aligns with active learning principles, where students construct knowledge through inquiry and reflection.

​Supporting Personalized and Adaptive Learning

These AI tools can tailor learning experiences to individual student needs. By analyzing student inputs, they can adjust the complexity and focus of information presented, thereby supporting differentiated instruction. This adaptability ensures that students remain challenged yet not overwhelmed, promoting sustained engagement and learning efficacy.​

 

Facilitating Collaborative Learning Environments

Generative AI can serve as a collaborative partner in group settings, assisting in brainstorming sessions or providing diverse perspectives on a topic. Their ability to process and generate information rapidly allows students to explore multiple facets of a subject, fostering a more comprehensive understanding through peer discussions and collaborative projects.

​Encouraging Metacognition and Self-Regulated Learning

By interacting with AI tools, students are prompted to reflect on their thought processes and learning strategies. This metacognitive engagement helps students become more aware of their cognitive processes, enabling them to regulate their learning more effectively. Such self-regulation is a key component of active learning, as it empowers students to take ownership of their educational journey.​

Incorporating generative AI tools like ChatGPT and Claude into educational practices offers a multifaceted approach to active learning, enhancing student engagement, personalization, collaboration, and self-regulation. Educators should thoughtfully integrate these technologies to complement traditional teaching methods, ensuring that the human element remains central to the learning experience.

Practical Applications

Implementing generative AI in active learning can take various forms, each enhancing student engagement and understanding:

​

• Case Studies and Simulations: AI can create realistic scenarios for students to analyze, promoting the application of theoretical knowledge.

Example Prompt: "Generate a case study involving a company facing ethical dilemmas in data privacy. Include background information, stakeholder perspectives, and potential consequences."​

• Immediate Feedback: AI-driven platforms can offer instant feedback on assignments, enabling students to reflect and improve in real-time.

Example Prompt: "Review the following essay on climate change for coherence, grammar, and argument strength. Provide constructive feedback and suggestions for improvement."​

• Interactive Tutorials: AI can guide students through complex problems step-by-step, adapting explanations to their learning pace.

Example Prompt: "Provide a detailed explanation of the photosynthetic process, emphasizing the molecular mechanisms of light-dependent and light-independent reactions."​

These applications not only enhance engagement but also foster a deeper understanding of the subject matter, aligning with the core principles of active learning.

Considerations for Implementation

While integrating AI into active learning environments offers significant potential, it's crucial to approach this integration thoughtfully.​

Maintain Human Oversight

Educators should actively guide generative AI interactions to ensure they align with learning outcomes and provide contextually appropriate content. Generative AI tools can support instruction by generating ideas or facilitating personalized learning paths, but they should not replace the educator's role in fostering critical thinking and contextual understanding. Establishing clear guidelines on AI usage helps maintain academic integrity and ensures that technology serves as an aid rather than a substitute for human instruction.

​Promote Critical Thinking

Encouraging students to question and evaluate AI-generated content fosters analytical skills and deeper engagement with the material. By critically assessing the accuracy and relevance of AI outputs, students develop the ability to recognize credible information and become more autonomous learners. Incorporating activities that require students to compare AI-generated responses with traditional research can enhance their evaluative skills and understanding of subject matter.

Ensure Accessibility

Selecting generative AI tools that are accessible to all students is essential to promote equity in the learning environment. Consideration should be given to diverse needs and backgrounds, including varying levels of digital literacy and access to technology. Providing training sessions, alternative resources, and support mechanisms ensures that all students can effectively engage with AI-enhanced learning activities. Additionally, choosing generative AI platforms that comply with accessibility standards helps accommodate students with disabilities, fostering an inclusive educational experience.

For guidance on writing effective prompts, refer to the Best Practices for Writing Effective Prompts.

Looking Ahead

As we continue to explore the synergy between generative AI and pedagogical strategies, our next focus will be on experiential learning. We'll examine how generative AI can create immersive experiences that bridge the gap between theory and practice.​

Stay tuned for the next article in our series: Generative AI Meets Experiential Learning.

Reference:

 

ElSayary, A. (2024). Integrating generative AI in active learning environments: Enhancing metacognition and technological skills. Journal of Systemics, Cybernetics and Informatics, 22(3), 34–37. https://doi.org/10.54808/jsci.22.03.34

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Washington, G. (2025, January 31). Generative AI Meets Active Learning [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

 

Generative AI Meets Universal Design for Learning

How can educators ensure learning experiences are accessible, inclusive, and engaging for every student? One promising approach is the integration of Generative AI within the Universal Design for Learning (UDL) framework. This article explores how Generative AI tools, such as ChatGPT, Gemini, and Claude, can support educators in creating flexible and inclusive learning environments. UDL guides educators in designing adaptable, student-centered instruction that meets diverse learner needs. By offering multiple ways for learners to engage, represent their understanding, and express their ideas, UDL creates meaningful opportunities for success.

New Possibilities with Generative AI

Generative AI’s ability to produce text, images, and code represents a pivotal shift in education. These tools open new possibilities for personalizing learning, fostering creativity, and improving accessibility for diverse learners (Saborío-Taylor & Rojas-Ramírez, 2024). To maximize its impact, educators should integrate Generative AI with frameworks like UDL to foster inclusive and engaging learning environments.

Generative AI tools can enhance both teaching and learning by managing routine tasks, such as summarizing readings or generating quiz questions, freeing educators to focus on fostering critical thinking and engagement. These tools also support personalized learning by adapting materials for diverse needs. For instance, Gemini can simplify complex text for different reading levels, while Claude can provide structured outlines or comparison tables for students with visual impairments. By offering multimodal content, Generative AI helps create more inclusive learning experiences that align with UDL principles.

What is Universal Design for Learning?

UDL is a framework that supports the creation of flexible learning environments to meet the needs of all learners. It emphasizes engaging, equitable, and accessible learning experiences for all students, regardless of ability. The UDL Guidelines, developed by the Center for Applied Special Technology (CAST), outline a comprehensive approach to UDL implementation. The updated guidelines emphasize equity and inclusion, recognizing that systemic barriers like bias and oppression can impact learning. UDL Guidelines 3.0 highlights the need for learning environments that affirm diverse identities and are free from bias (CAST, 2024).

UDL’s Core Principles

The three core principles of UDL remain central to creating inclusive learning environments:

Multiple Means of Engagement. Motivating and supporting students with diverse interests and learning needs. This includes strategies for recruiting interest (connecting to student passions), sustaining effort through collaboration and feedback, and fostering self-regulation skills like goal setting and reflection.

Multiple Means of Representation. Presenting information in different formats to support diverse sensory needs and learning preferences. This includes using multiple formats (text, audio, video), adjusting the pace of information, highlighting key concepts, and providing glossaries and visual aids.

Multiple Means of Action and Expression. Providing varied ways for students to demonstrate their learning. This includes options for physical interaction (e.g., manipulating objects), tools for expression (e.g., writing, speaking, creating visuals), and support for executive functions, such as planning and organizing learning tasks.

Equity and Inclusion in UDL Guidelines 3.0

The updated UDL principles now emphasize equity and inclusion more explicitly. For example, Multiple Means of Representation now includes authentic representation of diverse identities, challenging biases in content, and valuing diverse ways of knowing and interpreting information. Implementing UDL Guidelines 3.0 allows educators to design more inclusive and effective learning experiences for all students.

How Generative AI Supports UDL Principles

Generative AI platforms like ChatGPT, Gemini, and Claude can play a significant role in supporting the implementation of UDL principles.

Multiple Means of Engagement

Generative AI can enhance learning through interactive and engaging experiences. For example, ChatGPT and Claude can support the creation of games, simulations, and immersive activities that encourage exploration and persistence. AI-powered tools can offer personalized feedback and encouragement, helping students persist in their learning and recognize their progress.

Multiple Means of Representation

Generative AI can create personalized learning materials, such as text=based summaries, guiding questions, and visual comparison tables, to support diverse sensory needs and learning preferences. For example, AI can simplify complex text, generate structured outlines to clarify key points, and provide creative prompts that encourage firsthand exploration and critical thinking.

Multiple Means of Action and Expression

Generative AI can empower students to express their learning creatively. For example, students can compose stories, poems, and essays, experiment with different writing styles, and refine their writing. They can also use AI to create personalized art, music, and multimedia projects, fostering innovative expression. Text-to-speech tools can help students with physical disabilities express ideas verbally and create presentations. Writing assistants can support students with learning disabilities by helping them organize and refine their writing.

By integrating Generative AI within a UDL framework, educators can design more equitable, inclusive, and engaging learning experiences for all students.

Examples of UDL and Generative AI in Action

Now that we explored the alignment between Generative AI and UDL principles, let us look at real-world examples of how these tools can enhance learning in action. Generative AI platforms offer educators flexible ways to engage students, represent content in multiple formats, and support different methods of expression. Below are practical examples demonstrating how these platforms can support each UDL principle in practice (OpenAI, 2024).

Multiple Means of Engagement

Scenario: A sociology professor wants to increase student engagement during a unit on social justice movements.

• Application: The professor uses Claude to create an interactive case study where students explore the impacts of different social policies on various communities. ChatGPT generates role-play prompts where students take on different stakeholder perspectives, such as activists, policymakers, and historians.
• Benefit: By involving students in active, real-world scenarios, the professor taps into student interests and creates opportunities for meaningful exploration and discussion.

Multiple Means of Representation

Scenario: A history professor teaching a course on global revolutions wants to ensure students with different learning preferences can access complex readings.

• Application: The professor uses Gemini to generate concise, accessible summaries of academic journal articles and Claude to produce comparison tables that highlight key differences between historical events. ChatGPT provides guiding questions that prompt students to make connections between the readings and broader themes.
• Benefit: By presenting the same information through summaries, tables, and guiding questions, the professor makes the material more comprehensible for all learners.

Multiple Means of Action and Expression

Scenario: In an English class, students demonstrate their understanding of a novel by creating final projects to reflect their comprehension.

• Application: One student uses ChatGPT to help outline their literary analysis essay, while another uses Claude to draft dialogue for a creative retelling of a scene. A third student uses Gemini to generate questions for a mock interview between themselves and the novel’s protagonist, which they turn into a written Q&A.
• Benefit: These options allow students to choose the format that best aligns with their strengths, fostering autonomy and creativity.

Ethical Considerations in Using Generative AI in Education

It is important to address the ethical challenges associated with using Generative AI in education (Bura & Myakala, 2024).

Data Privacy and Security

Generative AI tools often require collecting and analyzing student data, raising privacy and security concerns. Student data must be collected, stored, and used responsibly, with safeguards to protect privacy.

Bias and Fairness in AI Algorithms

Large datasets train AI algorithms, which may contain biases that the systems can perpetuate or amplify. Ensuring that AI systems in education are fair, unbiased, and non-discriminatory is essential.

The Potential for Misuse of AI Tools by Students

Students may misuse AI tools to plagiarize or generate misleading information. This includes providing clear guidelines for ethical AI use, strategies for detecting AI-generated content, and lessons on the ethical implications of AI.

Lack of Teacher Training and Support

Teachers need the skills to use AI tools effectively, understand their ethical implications, and address integration challenges. Ongoing professional development is essential to help teachers leverage AI to enhance student learning.

Ensuring Equitable Access to AI Technology for All Students

Not all students have equitable access to AI technology and high-speed internet. Addressing the digital divide ensures all students have access to AI technology and support to use it effectively.

Maintaining Human Connection and Authentic Learning Experiences

AI use in education must not detract from human connection and authentic learning experiences. AI can enhance learning but should not replace the human connections between teachers and students.

Conclusion: The Future of UDL and Generative AI

By applying UDL principles and addressing ethical concerns, educators can integrate Generative AI tools like ChatGPT, Gemini, and Claude to create personalized, inclusive, and engaging learning experiences. This requires ongoing research, collaboration across disciplines, and professional development to ensure these technologies enhance, rather than replace, human connection in education.

References:

Bura, C., & Myakala, P.K. (2024). Advancing transformative education: Generative AI as a catalyst for Equity and innovation.

CAST (2024). Universal Design for Learning Guidelines version 3.0. https://udlguidelines.cast.org

OpenAI. (2024). ChatGPT (December 31 version) [Large language model]. https://chat.openai.com

Saborío-Taylor, S., & Rojas-Ramírez, F. (2024). Universal design for learning and artificial intelligence in the digital era: Fostering inclusion and autonomous learning. International Journal of Professional Development, Learners and Learning, 6(2). https://doi.org/10.30935/ijpdll/14694

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Washington, G. (2024, December 31). Generative AI Meets Universal Design for Learning [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image by Gerd Altmann from Pixabay

 

Generative AI Meets Pedagogy

Generative Artificial Intelligence (AI) emerged as a powerful tool that can transform teaching and learning. As educators, we are constantly seeking innovative ways to engage our students and enhance their learning experiences. This blog series, Generative AI Meets Pedagogy, aims to explore the intersection of Generative AI and effective teaching strategies, providing practical applications for educators.

Why Focus on Pedagogy?

The key to successful integration lies in aligning Generative AI with sound pedagogical practices. Pedagogy is the art and science of teaching, and it encompasses various approaches that can significantly impact student engagement and success. In this blog series, we will delve into three key pedagogical frameworks.

• Universal Design for Learning (UDL) emphasizes accessibility and inclusivity, ensuring that all students have equal opportunities to learn. UDL principles guide educators in creating flexible, inclusive learning environments that accommodate diverse learners.
• Active learning encourages student engagement and participation in the learning process. It involves activities that require students to think critically, problem-solve, and apply their knowledge.
• Experiential Learning focuses on learning through experience and reflection. It often involves hands-on activities, simulations, real-world scenarios, and immersive learning experiences.

Educators often use these pedagogical frameworks to create effective and engaging learning experiences.

The Role of Generative AI

Generative AI has the potential to enhance these pedagogical approaches in significant ways. Educators can create personalized learning experiences, generate tailored content, facilitate collaboration, and engage students in real-world problem-solving. The integration of Generative AI not only supports diverse learners but also empowers educators to innovate their teaching practices.

Join Us on This Journey

In the upcoming articles, we will explore the intersection of Generative AI and one of the pedagogical frameworks mentioned above. Each article will highlight the potential of Generative AI to enhance teaching and learning experiences and include actionable strategies for educators to implement in their classrooms.

We invite you to follow along as we explore how Generative AI can meet pedagogy in transformative ways. Whether you are a seasoned educator or just starting your teaching journey, this series aims to provide valuable insights that can enhance your practice and enrich your students’ learning experiences.

Until we meet again in December, check out the blog article, Teaching Students Responsible Use of Generative AI. _________________________________________________________________________________________

Scholarly Journal Articles

Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S.K. (2024). Generative AI in
education: Pedagogical, theoretical, and methodological perspectives. International Journal
of Technology in Education (IJTE), 7(3), 373-385. https://doi.org/10.46328/ijte.845

Nikolopoulou, K. (2024). Generative Artificial Intelligence in Higher Education: Exploring Ways of Harnessing Pedagogical Practices with the Assistance of ChatGPT. International Journal of Changes in Education, 1(2), 103–111. https://doi.org/10.47852/bonviewIJCE42022489
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Washington, G. (2024, August 31). Generative AI Meets Pedagogy [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image generated by Microsoft Designer.

Teaching Students Responsible Use of Generative AI

The educational landscape is constantly evolving, and the emergence of generative artificial intelligence (AI) presents both exciting possibilities and significant challenges.  Large language models (LLMs) such as ChatGPT, Gemini, and Claude 3 can produce human-quality text, code, and even creative content. This can enhance learning experiences by personalizing instruction, fostering creativity, and providing immediate feedback. However, alongside the excitement, addressing the ethical considerations and potential pitfalls of this powerful AI technology is vital.

Simply put, generative AI can be a double-edged sword. While it can be a valuable tool to support learning, it can also lead to plagiarism, the spread of misinformation, and an over-reliance on AI-generated solutions. To ensure generative AI becomes a tool for empowerment rather than a crutch, promoting responsible use and fostering critical thinking skills are fundamental. This article explores strategies for navigating the world of generative AI in the classroom, equipping students to use this technology effectively while developing the critical thinking skills necessary to become informed consumers of information in the digital age.

Here are some practical strategies you can implement to guide your students toward responsible AI use:

Transparency and Open Discussion:

• Start with the Basics: Begin by introducing students to the concept of generative AI, its capabilities, and limitations. Discuss how these models work and the types of content they can generate.
• Open Dialogue: Facilitate discussions about the ethical implications of AI-generated content. Explore issues like plagiarism, bias, and the spread of misinformation.
• Expectations and Guidelines: Clearly outline your expectations regarding generative AI use in your classroom. Specify when and how AI tools can be used for assignments and projects.

Fostering Critical Thinking and Evaluation:

• Fact-Checking and Verification: Incorporate activities that require students to evaluate the accuracy and credibility of AI-generated content. Teach them to use reliable sources to fact-check information and identify potential biases.
• Source Attribution and Citation: Emphasize the importance of citing sources, even when using AI-generated content as a starting point. Discuss plagiarism policies and responsible use of intellectual property.
• Critical Analysis Skills: Develop activities that encourage students to analyze AI-generated content for accuracy, logic, and coherence.

Leveraging AI for Enhanced Learning:

• Brainstorming and Idea Generation: Encourage students to use generative AI tools for brainstorming new ideas, generating creative prompts, or outlining arguments for essays.
• Personalized Learning: Explore AI-powered platforms that offer individualized learning paths based on student strengths and weaknesses.
• Feedback and Revision: Utilize generative AI tools that provide automated feedback on grammar, style, and clarity. This can be a valuable aid in the revision process, but should not replace human evaluation.

Developing Skills Beyond AI:

• Prioritize Critical Thinking: While generative AI can be a powerful tool, emphasize the importance of independent thinking, research skills, and problem-solving abilities.
• Communication and Collaboration: Focus on developing strong communication and collaboration skills that machines cannot replicate.
• Human Creativity: Encourage students to explore creative endeavors that leverage their imagination and go beyond what AI can currently generate.

By implementing these strategies, you can foster a culture of responsible generative AI use in your classroom. Students will develop critical thinking skills, become informed consumers of information, and learn to leverage AI as a powerful tool for enhancing learning, not replacing it. Remember, generative AI is still under development, and ongoing discussions about its implications are crucial. Embrace this new technology as an opportunity to equip students with the skills they need to thrive in an AI-powered future.

Resources:

Mollick, E. R., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4475995

Walczak, K., & Cellary, W. (2023). Challenges for higher education in the era of widespread access to generative AI. Economics and Business Review, 9(2). https://doi.org/10.18559/ebr.2023.2.743

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Washington, G. (2024, April 30). Teaching Students Responsible Use of Generative AI [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image Generated with AI

Navigating the Generative AI Landscape

The emergence of generative artificial intelligence (AI) presents exciting possibilities for teaching and learning. These large language models generate text, translate languages, write different kinds of creative content, and even answer users' questions. But with several options, deciding which generative AI to explore is daunting. In this article, we briefly explore ChatGPT, Bard, and Claude to help you navigate the generative AI landscape.

ChatGPT (Open AI) excels at generating creative text formats, like poems, code, scripts, musical pieces, and even emails. As a creative text generator, its strength lies in its conversational fluency, engaging users in open-ended dialogues, and producing creative text formats. Its ability to adapt to different writing styles and tones makes ChatGPT a valuable tool for brainstorming ideas, exploring different perspectives, and practicing writing skills. However, ChatGPT's focus on creativity can sometimes come at the expense of factual accuracy. Its outputs may lack citations or references, and its understanding of complex topics is sometimes limited. However, concerns regarding potential bias and misinformation in ChatGPT necessitate careful monitoring and face-checking of its responses (Sætra, 2023).

On the other hand, Bard (Google AI) prioritizes factual accuracy and information retrieval. Bard leverages Google’s vast knowledge base to provide informative and comprehensive answers. Bard is great at summarizing articles and papers or generating reports. It provides citations and references to support its claims, making it reliable for research and academic writing. However, Bard's focus on factual accuracy sometimes makes its responses seem less creative or engaging than ChatGPT. Bard’s ability to generate creative text formats is still under development (Tan, Chen, & Chua, 2023).

Claude strikes a balance between ChatGPT’s creativity and Bard’s factual accuracy but prioritizes ethical interactions and responsible AI development (Anthropic, 2023). Claude excels at identifying and mitigating potential biases in its responses, making it suitable for tasks requiring sensitive information or ethical considerations and promoting inclusive learning environments. However, Claude is still under development and may not be as refined as ChatGPT or Bard in some areas. (Gabriel, 2020).

In summary, ChatGPT, Bard, and Claude are key players in the generative AI landscape. They possess unique features and potential applications. For generating writing prompts, creating outlines, crafting distinct character voices, or assisting with brainstorming ideas, ChatGPT acts as a writing partner. Bard and Claude are options for fostering historical understanding and encouraging critical thinking. Bard’s online access facilitates accurate information on historical events and figures. Claude, with its emphasis on safety and minimizing bias, provides fair and objective debate opportunities. Bard and ChatGPT hold potential as personalized learning assistants with Bard's responses being more factual while ChatGPT's engaging conversational style makes learning more interactive. For assisting with coding practice, both Bard and Claude demonstrate promise. Bard’s access to technical information is invaluable in explaining complex concepts or identifying errors in student code. Claude’s commitment to safety fosters a secure learning environment. As these models continue to learn and improve their potential to assist users will continue to grow.

References

Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds & Machines 30(3), 411–437. https://doi.org/10.1007/s11023-020-09539-2

Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society, 75, 102372. https://doi.org/10.1016/j.techsoc.2023.102372

Tan, S. C., Chen, W., & Chua, B. L. (2023). Leveraging generative artificial intelligence based on large language models for collaborative learning. Learning: Research & Practice, 9(2), 125–134. https://doi.org/10.1080/23735082.2023.2258895

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Washington, G. (2024, January 31). Navigating the Generative AI Landscape [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

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