Establish mandatory data protection measures before requiring student GenAI use, identify privacy violations, and select strategies to protect students from inappropriate outputs. 

Key Topics: 

1. Determining mandatory data protection measures for third-party GenAI tools. 
2. Identifying institutional privacy agreements required with GenAI vendors. 
3. Recognizing scenarios that violate regional data privacy regulations (GCC laws). 
4. Selecting ethical strategies to counter inappropriate or culturally insensitive outputs. 
5. Identifying sandboxing and vetted EdTech GenAI as proactive protection strategies. 

Classify student-created GenAI content for intellectual property purposes, identify legal risks in proprietary materials, and determine correct citation formats for AI outputs. 

Key Topics: 

1. Classifying GenAI-assisted student content as human-authored IP or non-human output. 
2. Distinguishing between prompt refinement and copy-paste for citation purposes. 
3. Identifying legal risks of copyright infringement in training data usage. 
4. Determining correct citation formats (APA, MLA) for GenAI outputs. 
5. Determining IP status of student tasks involving GenAI collaboration. 

Analyze how GenAI homogenization impacts critical thinking, select effective faculty interventions, and match GenAI use cases to UNESCO principles being violated. 

Key Topics:

1. Analyzing how homogenization of opinions by GenAI negatively impacts postgraduate research. 
2. Selecting effective faculty interventions to encourage challenging GenAI outputs. 
3. Analyzing consequences of reduced diversity in theoretical frameworks. 
4. Implementing fact-checking and empirical testing requirements for students. 
5. Matching student GenAI use scenarios to corresponding UNESCO principles (Human Agency, Pluralism).