1. AI-supported inquiry-based learning:

AI-supported inquiry-based learning: In this approach, students collaborate with AI to develop research questions and hypotheses related to real-world issues. The AI can help identify relevant resources, but students must analyze and synthesize information to draw conclusions. This teaching method emphasizes critical thinking, information literacy, and ethical decision-making. To address assessment challenges, educators can scaffold the process with milestones, such as research question submission, literature review, and final conclusions.

AI-supported inquiry-based learning Examples:

IT Path Example: Students in a cybersecurity course collaborate with AI to investigate the current trends and challenges in securing IoT (Internet of Things) devices. They use AI to gather resources on the most recent security breaches, attack vectors, and effective protection measures. Students analyze the data and present their findings on potential solutions to enhance IoT security.

Business Path Example: In a marketing course, students work with AI to explore the impact of social media influencers on consumer behaviour. They gather data on engagement, reach, and conversion rates for different types of influencers and platforms. Students synthesize this information to create a strategy for a hypothetical company to optimize its influencer marketing campaigns.

Healthcare Path Example: Students in a public health course use AI to examine the relationship between air pollution and respiratory illnesses in urban areas. They gather data on pollution levels, disease prevalence, and other relevant factors. Students analyze this information, identifying correlations and potential causal relationships, and propose policy recommendations to mitigate health risks.

Education Path Example: In an educational psychology course, students collaborate with AI to investigate the effects of different teaching methodologies on student engagement and learning outcomes. They gather resources on various teaching approaches, such as flipped classrooms, project-based learning, and gamification, along with relevant research on their effectiveness. Students analyze these resources and develop recommendations for implementing the most effective teaching strategies in a given context.

Arts and Humanities Path Example: Students in a cultural studies course collaborate with AI to explore the impact of globalization on cultural identity. They gather resources on the fusion of cultures, the preservation of local traditions, and the role of media and technology in shaping cultural experiences. Students analyze the information to draw conclusions on the balance between cultural exchange and the maintenance of cultural uniqueness.

Learning outcomes:

a. Critical thinking skills: Students will learn to evaluate various sources of information, identify potential biases, and assess the credibility of the resources provided by AI. They will also learn to distinguish between relevant and irrelevant information, as well as to identify patterns, discrepancies, and logical connections.

b. Information literacy: Students will gain proficiency in navigating and utilizing different information sources, such as academic articles, datasets, and multimedia resources. They will also develop skills in searching for information efficiently, filtering and organizing it effectively, and discerning the credibility and authority of various sources.

c. Ethical decision-making: Students will be exposed to various ethical considerations in the research process, such as data privacy, research integrity, and potential societal impacts. They will develop the ability to recognize ethical dilemmas and make informed, responsible decisions that consider the implications of their research on individuals, communities, and the environment.

d. Collaboration and communication: Students will learn to work effectively with AI, utilizing its capabilities to enhance their research and analysis while still maintaining an active role in the process. They will also develop the ability to communicate their findings clearly and persuasively, both in written and oral forms.

e. Research design and methodology: Students will learn to formulate research questions and hypotheses, design appropriate research methodologies, and select relevant tools and techniques to gather and analyze data. This includes qualitative and quantitative approaches, as well as mixed-methods research designs.

f. Problem-solving and adaptability: By engaging in inquiry-based learning, students will develop the ability to approach complex, real-world problems and devise innovative solutions. They will also learn to adapt to new information and challenges, refining their research questions and methodologies as needed.

g. Self-reflection and metacognition: Through self-assessments and reflection on the learning process, students will develop an awareness of their own strengths and weaknesses, as well as the strategies they use to learn effectively. This metacognitive skill will help them become more autonomous and self-directed learners.

By providing a structured framework for AI-supported inquiry-based learning, educators can help students develop a wide range of skills essential for academic and professional success in the 21st century, while minimizing the vulnerabilities associated with overreliance on AI-generated resources.

Pros, Cons and Vulnerabilities - Pros: Develops critical thinking, information literacy, and ethical decision-making skills. - Cons: Assessment difficulties; potential lack of student engagement. - Vulnerabilities: Overreliance on AI-generated resources; insufficient analysis and synthesis of information.