Table 6 Comparative analysis of CognifyNet and prior studies.
Study/Model | Technique | Key contributions | Limitations |
|---|---|---|---|
Woolf et al. (2013) | Chatbots and AI for learning survey | Insights into student perspectives on AI adoption in Sweden | Limited to a specific geographical context |
Cope et al. (2021) | Google Translate in language learning | Improved language proficiency through technology | Dependency on translation accuracy |
Farooq (2020) | ChatGPT in assessments | Questioned AI’s potential in revolutionizing assessment methods | Focused on basic feedback tools like ChatGPT |
Ahmad et al. (2023) | Speech-to-text for accessibility | Enhanced learning engagement through improved accessibility | Lacked predictive capabilities and personalization |
ChatGPT as a co-writing partner | Improved engagement and argumentative skills | Limited to collaborative writing tasks | |
Ethical analysis of AI in education | Addressed privacy and bias concerns in educational AI | Did not propose practical solutions for bias mitigation | |
Our proposed model (CognifyNet) | Hybrid ensemble + neural network | High predictive accuracy, personalization, cognitive and engagement analytics, bias mitigation | Computational complexity requires a robust infrastructure. |
