AI-Enabled Tools: Shaping the Future of Technology

Authors

  • Venkata Rama Padmaja Chinimilli Institute of Aeronautical Engineering
  • Priyanka Kumari Bhansali Andhra University image/svg+xml
  • J Sirisha Devi Koneru Lakshmaiah Education Foundation image/svg+xml
  • Sukanya Ledalla Koneru Lakshmaiah Education Foundation image/svg+xml
  • Mary Swarna Latha Gade Institute of Aeronautical Engineering

DOI:

https://doi.org/10.4108/eetsis.8213

Keywords:

AI-enabled tools, chatbots, ChatGPT, AI image generation, AI text generation, CodeGPT

Abstract

INTRODUCTION: AI-enabled tools are revolutionizing various fields, ushering in a transformative era for technology and industry. These advancements impact diverse sectors, from customer service and industrial design to cybersecurity and computer vision, reshaping human-computer interactions.
OBJECTIVES: This article explores the applications and advancements of AI tools, focusing on chatbots, image and text generation, literature review research, AI-driven coding tools, and emerging applications in cybersecurity and media analysis. It also provides an overview of publicly available AI tools and conducts a comparative analysis of leading chatbots.
METHODS: A comprehensive review and analysis were conducted on tools including ChatGPT, CodeGPT, and AI systems for image and text generation. The study also examined AI applications in spear phishing defense, facial recognition across age variations (FaceNet), and deepfake detection, alongside a comparative analysis of chatbots such as ChatGPT, Google Bard, LLaMA, and MS Bing.
RESULTS: The exploration revealed that chatbots like ChatGPT have redefined customer service, while AI tools for image generation impact art, medical imaging, and industrial design. AI-driven text generation and coding tools enhance content creation and software development efficiency. Additionally, AI applications in cybersecurity, facial recognition, and deepfake detection demonstrate the technology’s growing societal relevance. Comparative analysis of chatbots highlighted their distinct capabilities across platforms.
CONCLUSION: AI-enabled tools are shaping the future of technology, driving innovation, and expanding possibilities across industries and societal domains. The findings emphasize the need for continued exploration, ethical application, and responsible deployment to maximize their potential while addressing associated challenges.

References

[1] OpenAI (2023) Introducing ChatGPT (Retrieved from https://www.openai.com/chatgpt)

[2] A. Elgammal, B. Liu, M. Elhoseiny and M. Mazzone (2017) CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms (arXiv preprint arXiv:1706.07068)

[3] D. Shen, G. Wu and H. I. Suk (2018) Deep learning in medical image analysis (Annual Review of Biomedical Engineering), vol. 19, pp. 221-248.

[4] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei and I. Sutskever (2019) Language models are unsupervised multitask learners (OpenAI Blog), vol. 1, no. 8, pp. 9.

[5] M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, ... and W. Zaremba (2021) Evaluating large language models trained on code (arXiv preprint arXiv:2107.03374)

[6] E. Adamopoulou and L. Moussiades (2020) Chatbots: History, technology, and applications (Machine Learning with Applications, vol. 2, p. 100006).

[7] O. Aydin and E. Karaarslan (2023) Is ChatGPT leading generative AI? What is beyond expectations (What is Beyond Expectations).

[8] H. Trng (2023) ChatGPT in education—a global and Vietnamese research overview.

[9] J. Rudolph, S. Tan, and S. Tan (2023) War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education (Journal of Applied Learning and Teaching, vol. 6, no. 1).

[10] M. J. Ali (2023) ChatGPT and lacrimal drainage disorders: Performance and scope of improvement (Ophthalmic Plastic and Reconstructive Surgery, vol. 39, no. 3, p. 221).

[11] S. Bhardwaz and J. Kumar (2023) An extensive comparative analysis of chatbot technologies - ChatGPT, Google Bard and Microsoft Bing (in 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 673–679).

[12] T. Wu, S. He, J. Liu, S. Sun, K. Liu, Q.-L. Han, and Y. Tang (2023) A brief overview of ChatGPT: The history, status quo and potential future development (IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1122–1136).

[13] R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al. (2022) LaMDA: Language models for dialog applications (arXiv preprint arXiv:2201.08239).

[14] X. Amatriain (2023) Transformer models: An introduction and catalog (arXiv preprint arXiv:2302.07730).

[15] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Roziere, N. Goyal, E. Hambro, F. Azhar, et al. (2023) Llama: Open and efficient foundation language models (arXiv preprint arXiv:2302.13971).

[16] L. Zhang (2023) Four tax questions for ChatGPT and other language models.

[17] J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans (2022) Cascaded diffusion models for high fidelity image generation (The Journal of Machine Learning Research, vol. 23, no. 1, pp. 2249–2281).

[18] P. Dhariwal and A. Nichol (2021) Diffusion models beat gans on image synthesis (Advances in neural information processing systems, vol. 34, pp. 8780–8794).

[19] S. AI (2022) Stablediffusion2.1 release (Accessed on August 3, 2023) [Online]. Available: https://stability.ai/blog/stablediffusion2-1-release7-dec-2022

[20] M.-Y. Liu, X. Huang, J. Yu, T.-C. Wang, and A. Mallya (2021) Generative adversarial networks for image and video synthesis: Algorithms and applications (Proceedings of the IEEE, vol. 109, no. 5, pp. 839–862).

[21] C. Saharia, W. Chan, H. Chang, C. Lee, J. Ho, T. Salimans, D. Fleet, and M. Norouzi (2022) Palette: Image-to-image diffusion models (ACM SIGGRAPH 2022 Conference Proceedings, pp. 1–10).

[22] A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen (2021) Glide: Towards photorealistic image generation and editing with text-guided diffusion models (arXiv preprint arXiv:2112.10741).

[23] J. R. Hershey and P. A. Olsen (2007) Approximating the Kullback-Leibler divergence between Gaussian mixture models (in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 4, pp. IV-317–IV-320, IEEE).

[24] X. Pan, A. Tewari, T. Leimkuhler, L. Liu, A. Meka, and C. Theobalt (2023) Drag your gan: Interactive pointbased manipulation on the generative image manifold (ACM SIGGRAPH 2023 Conference Proceedings, pp. 1–11).

[25] Y. Li, M. Min, D. Shen, D. Carlson, and L. Carin (2018) Video generation from text (Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32).

[26] U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni, et al. (2022) Make-a-video: Text-to-video generation without text-video data (arXiv preprint arXiv:2209.14792).

[27] H. Lin, A. Zala, J. Cho, and M. Bansal (2023) Videodirectorgpt: Consistent multi-scene video generation via llm-guided planning (arXiv preprint arXiv:2309.15091).

[28] E. Loper and S. Bird (2002) NLTK: The natural language toolkit (arXiv preprint cs/0205028).

[29] Y. Vasiliev (2020) Natural language processing with Python and spaCy: A practical introduction (No Starch Press).

[30] T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, et al. (2019) Huggingface’s transformers: State-of-the-art natural language processing (arXiv preprint arXiv:1910.03771).

[31] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut (2019) ALBERT: A lite BERT for selfsupervised learning of language representations (arXiv preprint arXiv:1909.11942).

[32] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov (2019) RoBERTa: A robustly optimized BERT pretraining approach (arXiv preprint arXiv:1907.11692).

[33] T. Reddy, R. Williams, and C. Breazeal (2021) Text classification for AI education (SIGCSE, p. 1381).

[34] J. Pachouly, S. Ahirrao, K. Kotecha, G. Selvachandran, and A. Abraham (2022) A systematic literature review on software defect prediction using artificial intelligence: Datasets, data validation methods, approaches, and tools (Engineering Applications of Artificial Intelligence, vol. 111, p. 104773).

[35] E. A. Van Dis, J. Bollen, W. Zuidema, R. van Rooij, and C. L. Bockting (2023) ChatGPT: Five priorities for research (Nature, vol. 614, no. 7947, pp. 224–226).

[36] K. Nguyen-Trung, A. K. Saeri, and S. Kaufman (2023) Applying ChatGPT and AI-powered tools to accelerate evidence reviews.

[37] N. Gleason (2022) ChatGPT and the rise of AI writers: How should higher education respond? (Times Higher Education).

[38] G. Cooper (2023) Examining science education in ChatGPT: An exploratory study of generative artificial intelligence (Journal of Science Education and Technology, vol. 32, pp. 444–452).

[39] Z. Epstein, A. Hertzmann, I. of Human Creativity, M. Akten, H. Farid, J. Fjeld, M. R. Frank, M. Groh, L. Herman, N. Leach, et al. (2023) Art and the science of generative AI (Science, vol. 380, no. 6650, pp. 1110–1111).

[40] L. Skavronskaya, A. H. Hadinejad, and D. Cotterell (2023) Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education (Journal of Teaching in Travel & Tourism, vol. 23, no. 2, pp. 253–258).

[41] J. Huang and K. C.-C. Chang (2023) Citation: A key to building responsible and accountable large language models (arXiv preprint arXiv:2307.02185).

[42] B. Yetiştiren, I. Ozsoy, M. Ayerdem, and E. Tuzun (2023) Evaluating the code quality of AI-assisted code generation tools: An empirical study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT (arXiv preprint arXiv:2304.10778).

[43] B. Roziere, J. Gehring, F. G. Baptiste, and J. G. e. a. (2023) Code Llama: Open foundation models for code (arXiv preprint arXiv:2304.10778).

[44] T. Calo and L. De Russis (2023) Leveraging large language models for end-user website generation (International Symposium on End User Development, pp. 52–61, Springer).

[45] A. M. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. C. Desmarais, and Z. M. J. Jiang (2023) GitHub Copilot AI pair programmer: Asset or liability? (Journal of Systems and Software, vol. 203, p. 111734).

[46] M. Mohamed, N. Nachaat, H. Taherdoost, and M. Madanchian (2025) Enhancing Spear Phishing Defense with AI: A Comprehensive Review and Future Directions (EAI Endorsed Transactions on Scalable Information Systems, vol. 12, no. 1).

[47] C. H. Chethana, et al. (2025) FaceNet – A Framework for Age Variation Facial Digital Images (EAI Endorsed Transactions on Scalable Information Systems, vol. 12, no. 1).

[48] D. Garg and R. Gill (2024) A Bibliometric Analysis of Deepfakes: Trends, Applications and Challenges (EAI Endorsed Transactions on Scalable Information Systems, vol. 11, no. 6).

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Published

09-10-2025

How to Cite

1.
Chinimilli VRP, Bhansali PK, Devi JS, Ledalla S, Latha Gade MS. AI-Enabled Tools: Shaping the Future of Technology. EAI Endorsed Scal Inf Syst [Internet]. 2025 Oct. 9 [cited 2025 Oct. 30];12(4). Available from: https://publications.eai.eu/index.php/sis/article/view/8213

Issue

Vol. 12 No. 4 (2025): EAI Endorsed Transactions on Scalable Information Systems

Section

Review article

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Copyright (c) 2025 Venkata Rama Padmaja Chinimilli, Priyanka Kumari Bhansali, J Sirisha Devi, Sukanya Ledalla, Mary Swarna Latha Gade

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This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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