# Generative artificial intelligence

In a six-week flipped EFL writing course, 58 second-year student teachers at a Thai university independently selected resources such as textbooks, websites, educational videos, and GenAI tools and synthesized their learning through handwritten notebook summaries; questionnaire responses showed generally positive perceptions of learning preparation, information management, learning responsibility, and writing readiness, while focus group discussions with 30 students revealed four interconnected constraints—limited linguistic and background knowledge, uncertainty without immediate teacher guidance, difficulty evaluating and synthesizing multiple resources, and challenges in negotiating GenAI use—which students navigated through increased effort, planning and self-regulation, use of multiple

In a six-week flipped EFL writing course, 58 second-year student teachers at a Thai university independently selected resources such as textbooks, websites, educational videos, and GenAI tools and synthesized their learning through handwritten notebook summaries; questionnaire responses showed generally positive perceptions of learning preparation, information management, learning responsibility, and writing readiness, while focus group discussions with 30 students revealed four interconnected constraints—limited linguistic and background knowledge, uncertainty without immediate teacher guidance, difficulty evaluating and synthesizing multiple resources, and challenges in negotiating GenAI use—which students navigated through increased effort, planning and self-regulation, use of multiple

This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.

This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.

This review surveys molecular modelling methods for aqueous batteries—DFT, DFTMD, empirical force-field MD, machine-learning force fields and generative AI—and uses water-in-salt electrolytes, transition-metal oxide cathodes, Zn-ion batteries and organic redox flow batteries as case studies to show how these methods reveal ion solvation, intercalation, electron transfer and interfacial structure, thereby explaining electrochemical stability windows, ion transport and dendrite suppression.

This review surveys molecular modelling methods for aqueous batteries—DFT, DFTMD, empirical force-field MD, machine-learning force fields and generative AI—and uses water-in-salt electrolytes, transition-metal oxide cathodes, Zn-ion batteries and organic redox flow batteries as case studies to show how these methods reveal ion solvation, intercalation, electron transfer and interfacial structure, thereby explaining electrochemical stability windows, ion transport and dendrite suppression.

This systematic review searched PubMed and Embase (July 9, 2025) and included 23 studies to assess generative AI, mainly ChatGPT 3.5/4, in total hip and knee arthroplasty across patient communication and education (n=19), clinical documentation (n=2), and clinical decision support (n=2): blinded ratings found FAQ responses comparable to surgeon-written answers in accuracy, clarity, and completeness with better readability; consent documents showed better readability and completeness than surgeon versions; operative-report extraction reached 97.5%–100% agreement; decision support showed higher accuracy for surgical candidacy but low specificity for outcome prediction, alongside fabricated citations and limited patient trust.

This systematic review searched PubMed and Embase (July 9, 2025) and included 23 studies to assess generative AI, mainly ChatGPT 3.5/4, in total hip and knee arthroplasty across patient communication and education (n=19), clinical documentation (n=2), and clinical decision support (n=2): blinded ratings found FAQ responses comparable to surgeon-written answers in accuracy, clarity, and completeness with better readability; consent documents showed better readability and completeness than surgeon versions; operative-report extraction reached 97.5%–100% agreement; decision support showed higher accuracy for surgical candidacy but low specificity for outcome prediction, alongside fabricated citations and limited patient trust.

This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.

This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.