Doaa Ezzat

Some studies and tools have been developed to assess the knowledge, practice, and attitudes (KAP) of students, particularly in the context of AI. You can further explore here:

• Overview ‹ Impact.AI: K-12 AI Literacy — MIT Media Lab https://www.media.mit.edu/projects/impact-ai-k-12/overview/

• Article Knowledge, Attitude, and Practices toward Artificial Intelli...

• Article Exploring knowledge, attitudes, and practices towards artifi...

Dear Doaa Ezzat ,

Background: The expansion of artificial intelligence (AI) across diverse sectors worldwide demands an understanding of its impact on future generations. The studies of its influence on university students’ behavior and application in Lebanon are still limited. The present study aimed to explore the knowledge, attitudes, and practices (KAPs) of university students regarding AI and to identify factors affecting these dimensions. Methods: An online questionnaire (n = 457) was distributed to university students who were at least 18 years of age across Lebanon. Results: The results revealed that a significant majority (97.2%) of the participants were familiar with AI, from which 43% demonstrated a high level of knowledge. Furthermore, attitude toward AI role and integration in academic and professional paths was moderately satisfactory (43%), although it was reportedly used by 75% of students throughout their university years. There was a significant association between knowledge levels and sociodemographic factors such as age, sex, source of AI-related information, and knowledge rating (p < 0.05), whereas the academic major and knowledge rating affected attitudes toward AI (p < 0.05). Conclusion: These findings support the incorporation of AI education within the curriculum to increase acceptance of AI as a modern tool enhancing various sectors and serving as a facilitator for teaching and learning processes.

Article Knowledge, Attitude, and Practices toward Artificial Intelli...

Regards,

Shafagat

To assess the knowledge, practice, and attitude of AI among internship students, you can use a combination of the following tools and methods:

Knowledge Assessment

• Multiple-Choice Questions (MCQs): Create a series of MCQs focused on AI concepts, algorithms, applications, and ethical considerations.
• Short Answer Questions: Include open-ended questions that require students to explain AI principles, challenges, and recent advancements.
• Online Quizzes: Platforms like Google Forms, Kahoot, or Quizlet can be used to administer and automatically grade quizzes.
• Case Studies: Present real-world AI problems and ask students to analyze and propose solutions, assessing their understanding of AI applications.

Practice Assessment

• Practical Assignments: Assign tasks like developing simple AI models, working on datasets, or coding exercises using Python, TensorFlow, or PyTorch.
• Project-Based Assessment: Evaluate students by having them complete a mini-project, such as creating a chatbot, image recognition system, or predictive model.
• Code Review Sessions: Conduct peer or instructor-led code reviews to assess the quality, efficiency, and understanding of AI code written by students.
• Hackathons or Competitions: Organize AI hackathons where students can apply their skills in a competitive environment.

Attitude Assessment

• Surveys/Questionnaires: Use Likert scale questions to gauge students' attitudes towards AI, including their enthusiasm, ethical concerns, and perceived value of AI in their future careers.
• Focus Group Discussions: Conduct group discussions to explore students' attitudes towards AI, their interest in pursuing AI-related careers, and their views on the societal impact of AI.
• Reflective Essays: Ask students to write essays on topics like "The Role of AI in Society" or "My Perspective on the Future of AI," which can reveal their attitudes and ethical considerations.
• Interviews: Conduct one-on-one interviews to delve deeper into students' attitudes towards AI, allowing for more nuanced and personalized feedback.

Combined Tools

• Pre- and Post-Assessment Surveys: Administer surveys before and after the internship to measure changes in knowledge, practice, and attitude.
• Peer Assessment: Encourage students to assess each other's knowledge and practice through collaborative exercises or group projects.
• Self-Assessment Tools: Provide self-assessment checklists or reflective journals where students can evaluate their own progress and attitudes throughout the internship.

These tools can help you comprehensively assess the AI knowledge, practical skills, and attitudes of internship students, providing insights into their learning outcomes and areas for further development.