Future research directions of AI-powered tools for literature reviews

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Future research directions of AI-powered tools for literature reviews

AI future research directions are centred around enhancing the capabilities and usability of literature review tools. Incorporating state-of-the-art NLP technologies, like Large Language Models (LLMs), can significantly improve the performance of these tools, making them more effective and reliable. There is a need to develop advanced interpretability techniques to build trust and provide deeper insights into AI models’ decision-making processes. Integrating semantic technologies and creating robust benchmarks and evaluation frameworks will allow for more objective comparisons and assessments of different literature review tools. By addressing these challenges, future AI-enhanced review tools can become more user-friendly and widely adopted, ultimately transforming the systematic review process and contributing to more efficient and comprehensive research methodologies.

ATLAS.ti’s AI Lab is continuously working to optimize its AI tools. It maintains transparency by allowing users to view original data behind AI-generated insights which ensures rigorous and precise analysis. Its AI Lab has developed unique tools that extend beyond basic AI functionalities, emphasizing user control and data residency options to address privacy concerns.

Incorporating state-of-the-art NLP technologies, like Large Language Models (LLMs), can significantly improve the performance of AI tools. Photo by Conny Schneide.

References

  1. Wagner, G., Lukyanenko, R., & Paré, G. (2021). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 209-226. https://doi.org/10.1177/02683962211048201

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