Generative Artificial Intelligence: A Systematic Review and Applications

Generative AI for personalized learning and automated assessments

Context: Integration of generative AI tools, including chatbots and advanced NLP models, into educational practices to enhance learning experiences, provide personalized support, and improve assessment methods across various educational settings, including classrooms and online platforms.

Implementation: Incorporation of AI-driven tools, such as ChatGPT and large language models, as well as training models on diverse datasets for improved accuracy. This includes deploying chatbots and using models like BERT and ELMo for enhanced language understanding and generation.

Outcomes: ['Personalized learning experiences', 'Improved accessibility', 'Round-the-clock availability of educational resources', 'Enhanced student engagement and support through tailored interactions', 'Improved text summarization, sentiment analysis, and named entity recognition in educational materials']

Challenges: ['Ethical concerns', 'Potential for misuse', 'Bias in training data', 'Need for ethical guidelines', 'Technical hurdles in implementation', 'Challenges in model interpretability', 'Data privacy concerns', 'Need for educators to monitor interactions']

Ethical

Generative AI can be misused for malicious purposes, such as creating deepfakes, and there are concerns about biases in AI responses and the ethical implications of using AI tools in education.

Proposed Solutions: Establish ethical guidelines and governance structures to ensure responsible use, and conduct regular audits of AI systems for bias along with establishing guidelines for ethical AI use in classrooms.

Security

Generative models may have vulnerabilities that lead to adversarial attacks.

Proposed Solutions: Develop security measures to protect generative models and enhance adversarial robustness.

Bias and Fairness

Generative models may perpetuate biases present in training data.

Proposed Solutions: Implement methods to detect and mitigate bias, and promote inclusivity in datasets.

Data Privacy

Generative models could unintentionally retain sensitive information from training data.

Proposed Solutions: Adhere to data protection regulations and use privacy-preserving techniques.

Interpretability

The 'black box' nature of generative algorithms makes it hard to understand their decisions.

Proposed Solutions: Research explainable AI methods to improve transparency.

Training

Educators and students may lack the necessary training to effectively utilize generative AI tools.

Proposed Solutions: Implement training programs and resources for educators to familiarize them with AI technologies.

Project Contact: Dr. Jianhua Yang

LLM Model Version: gpt-4o-mini-2024-07-18

Analysis Provider: Openai