IEEE Transactions on Artificial Intelligence

About IEEE Transactions on Artificial Intelligence

IEEE Transactions on Artificial Intelligence (abbreviated IEEE Trans. Artif. Intell., commonly cited as IEEE TAI) is a multidisciplinary, peer-reviewed scholarly journal devoted to the theories, methodologies, and applications of artificial intelligence. Published by the Institute of Electrical and Electronics Engineers (IEEE) in the United States, it was launched in 2020 as the flagship artificial-intelligence journal of the IEEE Computational Intelligence Society, and it is produced through a collaboration of five IEEE societies: the IEEE Computational Intelligence Society (the managing partner), the IEEE Computer Society, the IEEE Systems, Man, and Cybernetics Society, the IEEE Signal Processing Society, and the IEEE Robotics and Automation Society. This five-society backing is unusual among AI journals and signals the breadth of communities the journal was built to serve.

The journal's stated scope is deliberately broad: "The IEEE Transactions on Artificial Intelligence (TAI) is a multidisciplinary journal publishing papers on theories and methodologies of Artificial Intelligence. Applications of Artificial Intelligence are also considered." Its aim is to publish impactful original research, survey articles, and real-world applications. Because it is young, TAI has grown up alongside the modern deep-learning era — large language models, generative AI, reinforcement learning, and AI safety were all rising to prominence as the journal published its first volumes — and its editorial scope has welcomed technical papers on contemporary topics including ethics and the social implications of AI.

TAI publishes monthly and carries the electronic ISSN 2691-4581. Articles are archived and distributed through IEEE Xplore, and the journal's bibliometric fields in IEEE Xplore are populated from Clarivate's Journal Citation Reports. The official journal homepage is hosted by the IEEE Computational Intelligence Society at cis.ieee.org, while manuscripts are submitted through the IEEE Author Portal at ieee.atyponrex.com/journal/tai-ieee.

History and Launch

The journal was formally announced in March 2020 as "the new journal our community has been waiting for to publish our work on Artificial Intelligence," in the words of the IEEE Computational Intelligence Society. According to the launch announcement carried by the IEEE Signal Processing Society (1 March 2020), the journal was made possible through the collaboration of the five IEEE societies listed above, with the Computational Intelligence Society acting as managing partner.

The launch timeline moved quickly:

• March 2020 — Journal announced; Founding Editor-in-Chief named as Prof. Hussein Abbass of the University of New South Wales (UNSW) Canberra, Australia.
• April 2020 — The manuscript submission site (then hosted on ScholarOne Manuscript Central) opened for submissions.
• August 2020 — The inaugural issue was published, with three issues planned for the launch year 2020.

The first volume already contained highly influential papers — notably a taxonomy of deep-learning model intellectual-property protection and a survey of graph learning — which helped establish the journal's reputation in its very first year. From those three issues in 2020, the journal has grown into a monthly publication: the October 2026 issue is Volume 7, Issue 10, published with 42 articles, reflecting a substantial increase in submission volume and editorial throughput over six years.

Aims and Scope

The journal's formal scope, repeated across its author instructions, special-issue guidelines, and review-process documentation, reads:

The IEEE Transactions on Artificial Intelligence (TAI) is a multidisciplinary journal publishing papers on theories and methodologies of Artificial Intelligence. Applications of Artificial Intelligence are also considered. The aim is to publish impactful research in Artificial Intelligence, survey articles, and applications.

Topics explicitly covered by IEEE TAI include, but are not limited to:

• Agent-based systems
• Augmented intelligence
• Autonomic computing
• Constraint systems
• Explainable AI
• Knowledge-based systems
• Learning theories
• Planning, reasoning, and search
• Natural language processing
• Real-world AI applications

Technical papers addressing contemporary topics in AI — including ethics and social implications — are expressly welcomed, which is why the journal has published work on responsible AI and human-rights best practices for industry alongside core machine-learning research.

Types of Contributions

According to the official author instructions, IEEE TAI publishes multiple contribution types, including:

• Original Research Regular Manuscripts — significant contributions with novel experimental, analytical, and/or theoretical findings. These may be application-focused, where the novelty lies in the methodology for applying an AI algorithm to a challenging application. The work must be self-contained, scientifically sound, evidence-based, and a conclusive treatment of the problem.
• Original Research Review Manuscripts — systematic surveys with a critical assessment of a scientific field, synthesizing existing research into a new form. A review article must articulate its review methodology, including inclusion and exclusion criteria, and the synthesis should lead to open research questions addressing significant gaps or new conceptual frameworks.

Editorial Board

Editor-in-Chief: Jun Wang, Chair Professor of Computational Intelligence, Department of Computer Science and School of Data Science, City University of Hong Kong, Hong Kong — per the official IEEE Computational Intelligence Society "IEEE Transactions on Artificial Intelligence 2025 Editorial Board" page (updated 3 April 2025).

Founding Editor-in-Chief: Prof. Hussein Abbass, University of New South Wales (UNSW) Canberra, Australia, who led the journal from its 2020 launch.

The journal is supported by a large international board of Associate Editors drawn from universities, research institutes, and industry laboratories across more than twenty countries. The 2025 board (per the official page) includes:

• Zahid Akhtar (State University of New York Polytechnic Institute, USA), Aparna Akula (Central Scientific Instruments Organisation, India), Plamen Angelov (Lancaster University, UK), Sansanee Auephanwiriyakul (Chiang Mai University, Thailand), Mehala Balamurali (University of Sydney, Australia), Sanghamitra Bandyopadhyay (Indian Statistical Institute Kolkata, India), Tanvi Banerjee (Wright State University, USA), Aaron Baughman (IBM, USA), Olfa Belkahla Driss (Ecole Supérieure de Commerce de Tunis, Tunisia), Anastasios Bezerianos (Information Technologies Institute, Greece), Ying Bi (Zhengzhou University, China), Hamid Bouchachia (Bournemouth University, UK), João Catalão (University of Porto, Portugal), C.L. Philip Chen (South China University of Technology, China), Shyi-Ming Chen (Asia University, Taiwan), Ran Cheng (Southern University of Science and Technology, China), Oscar Cordón (University of Granada, Spain), Keeley Crockett (Manchester Metropolitan University, UK), Dipankar Dasgupta (The University of Memphis, USA), Guilherme DeSouza (University of Missouri, USA), Weiping Ding (Nantong University, China), Yucheng Dong (Sichuan University, China), Yali Du (King's College London, UK), Pierpaolo D'Urso (University of Rome, Italy), Mehmet Efe (Hacettepe University, Türkiye), Mohammed El-Abd (American University of Kuwait, Kuwait), Pablo Estevez (University of Chile, Chile), Ali Etemad (Queen's University, Canada), Giancarlo Fortino (University of Calabria, Italy), Alejandro Frangi (University of Manchester, UK), Huazhu Fu (Agency for Science, Technology and Research, Singapore), G.R. Gangadharan (National Institute of Technology Tiruchirappalli, India), Chiara Gallese (University of Turin, Italy), Alexander Gegov (University of Portsmouth, UK), Hani Hagras (University of Essex, UK), Zahid Halim (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan), Ibrahim Hameed (Norwegian University of Science and Technology, Norway), Sean Holden (University of Cambridge, UK), Sameera Horawalavithana (Pacific Northwest National Laboratory, USA), Xiao-Min Hu (Guangdong University of Technology, China), Yo-Ping Huang (National Taipei University of Technology, Singapore), Tingwen Huang (Shenzhen University of Advanced Technology, China), Robert Hunjet (Defence Science and Technology Group, Australia), Amir Hussain (Napier University, UK), Omar Hussain (UNSW Canberra, Australia), Aya Hussein (University of Canberra, Australia), Alexandros Iosifidis (Aarhus University, Denmark), Yumi Iwashita (NASA JPL, China), Chaker Abdelaziz Kerrache (University of Laghouat, Algeria), Deepika Koundal (University of Petroleum & Energy Studies, India), Neeraj Kumar (Thapar University Patiala Punjab, India), Albert Lam (Fano Labs, Hong Kong), Annabel Latham (Manchester Metropolitan University, UK), Boaz Lerner (Ben-Gurion University of the Negev, Israel), Junhua Li (University of Essex, UK), Qing Li (Hong Kong Polytechnic University, Hong Kong), Xiang Li (Massachusetts General Hospital and Harvard Medical School, USA), Xiaoli Li (Agency for Science, Technology and Research, Singapore), Kaitai Liang (TU Delft, The Netherlands), Joo-Hwee Lim (Agency for Science, Technology and Research, Singapore), Chin Teng Lin (University of Technology Sydney, Australia), Jialin Liu (Lingnan University, Hong Kong), Lin Liu (University of South Australia, Australia), Qingwen Liu (Tongji University, China), Qinghua Lu (CSIRO, Australia), Hao Luo (Harbin Institute of Technology, China), Dwarikanath Mahapatra (Inception Institute of Artificial Intelligence, UAE), Letizia Marchegiani (University of Parma, Italy), Panagiotis (Panos P.) Markopoulos (The University of Texas at San Antonio, USA), Yi Mei (Victoria University of Wellington, New Zealand), Supratik Mukhopadhyay (Louisiana State University, USA), Anand Nayyar (Duy Tan University, Viet Nam), Hung Nguyen (The University of Adelaide, Australia), Geoff Nitschke (University of Cape Town, South Africa), Ben Niu (Shenzhen University, China), Marco Nobile (Ca' Foscari University of Venice, Italy), Gustavo Olague (Ensenada Center for Scientific Research and Higher Education, Mexico), Diego Oliva (Universidad de Guadalajara, Mexico), Yew Soon Ong (Nanyang Technological University, UK), Karen Panetta (Tufts University, USA), Alvaro Parra (Seeing Machines, Australia), Anand Paul (Kyungpook National University, South Korea), Francesco Piccialli (University of Naples Federico II, Italy), Mihail Popescu (University of Missouri, USA), Manuel Roveri (Politecnico di Milano, Italy), Monjoy Saha (National Cancer Institute, USA), Sriparna Saha (Indian Institute of Technology Patna, India), KC Santosh (University of South Dakota, USA), Teena Sharma (The University of Tennessee, India), Chao Shen (Xi'an Jiaotong University, China), Peng Shi (Adelaide University, Australia), Houbing Song (Embry-Riddle Aeronautical University, USA), Peter Stuckey (Monash University, Australia), Xiaojie Su (Chongqing University, China), Koduvayur Subbalakshmi (Stevens Institute, USA), Chaoli Sun (Taiyuan University of Science and Technology, China), Johan Suykens (Katholieke Universiteit Leuven, Belgium), KC Tan (City University of Hong Kong, Hong Kong), Jim Torresen (University of Oslo, Norway), Ivor Tsang (Agency for Science, Technology and Research, Singapore), Kanishka Tyagi (Aptiv Advance Research Center, USA), Kyriakos Vamvoudakis (Georgia Institute of Technology, USA), Thanos Vasilakos (University of Agder, Norway), Alvaro Velasquez (DARPA, USA), Marley Vellasco (Pontifícia Universidade Católica do Rio de Janeiro, Brazil), Nishchal Verma (Indian Institute of Technology Kanpur, India), Vincenzina Vitale (Sapienza University of Rome, Italy), Christian Wagner (University of Nottingham, UK), Min Wang (University of Canberra, Australia), Xiao Wang (Beihang University, China), Qinglai Wei (Chinese Academy of Sciences, China), Michael Wooldridge (Oxford University, UK), Feng Xia (RMIT University, Australia), Bing Xue (Victoria University of Wellington, New Zealand), Simon Yang (University of Guelph, Canada), Lina Yao (CSIRO, Australia), Xin Yao (Lingnan University, Hong Kong), Gary Yen (Oklahoma State University, USA), Zhi-Hui Zhan (Nankai University, China), Jingfeng Zhang (RIKEN, Japan), Jun Zhang (Hanyang University, Hong Kong), Dongbin Zhao (University of Chinese Academy of Sciences, China), and Jia Zhu (Zhejiang Normal University, China).

Note: the editorial board is updated periodically; the authoritative current list is maintained on the official page at cis.ieee.org.

Publication Model and Article Processing Charges

IEEE's official title list (November 2023) classifies IEEE Transactions on Artificial Intelligence as a Hybrid Open Access journal. That means authors can publish through the traditional subscription route or choose to make their individual article open access by paying an article processing charge (APC), with open-access articles freely readable on IEEE Xplore.

Important disclosure: the journal's own author-instruction pages (at the time this profile was researched) did not publish a TAI-specific APC figure, so the exact fee is stated here as not officially confirmed from the journal's own pages. IEEE maintains a central, regularly updated "IEEE Article Processing Charges" list (PDF, ieeeauthorcenter.ieee.org) in which hybrid open-access journals carry journal-specific fees; authors should confirm the current TAI APC on the submission site or in the IEEE Author Portal before submitting. Separately, IEEE journals traditionally apply overlength page charges for manuscripts exceeding the standard page limit — authors should check the current author instructions for the applicable per-page charge.

Submission and Peer Review Process

Manuscripts are submitted through the IEEE Author Portal at ieee.atyponrex.com/journal/tai-ieee (the journal previously used ScholarOne Manuscript Central at mc.manuscriptcentral.com/tai-ieee during its launch phase). Paper templates are available through IEEE's template selector at template-selector.ieee.org.

Double-Anonymous Review

IEEE TAI uses a double-anonymous (double-blind) review policy: authors must exclude any author-identifying information from their PDF submissions so that reviewers cannot identify them, and reviewers remain anonymous to authors.

The Review Workflow (per the official Review Process document)

1. Before submission — authors are expected to read the review-process documentation and hold an account on the IEEE Author Portal.
2. Editorial-assistant compliance check — the submission is first screened for: file integrity, double anonymization, appropriate formatting, page-limit compliance, and ethics compliance for papers involving human and/or animal experiments. Manuscripts failing any of these checks are returned to the authors.
3. Plagiarism check — a similarity report is run. The maximum allowable similarity index is 20% (assuming quoted and cited statements); manuscripts exceeding the threshold receive a straight rejection.
4. Peer review — consistent with IEEE practice, each article is evaluated by at least two independent reviewers selected from the publication's editorial community, assessing originality, significance, and technical soundness.

The official author pages do not publish an average review duration or acceptance rate; those figures are stated here as not officially available. Some third-party journal-review aggregators suggest review decisions typically take several months, but such figures are unofficial and unverified.

Indexing and Bibliometrics

Indexing: IEEE TAI is indexed in Scopus (Scopus coverage 2020–2026 reported by journal-metric aggregators). The journal's bibliometric fields in IEEE Xplore are populated from Clarivate's Journal Citation Reports — the official journal page notes that "the values displayed for the journal bibliometrics fields in IEEE Xplore are based on the Journal Citation Report from Clarivate from the 2024 report released in June 2025." Articles carry Crossref DOIs of the form 10.1109/TAI.xxxx (e.g., 10.1109/TAI.2024.3394389).

Metrics (third-party aggregators — labeled as such): Because official pages display but do not tabulate the current metrics, the following figures come from independent journal-metric sites and should be treated as indicative rather than official:

• Impact score (Scopus-based): ~6.07 for 2025 (Resurchify); Exaly reports an impact factor of ~6.1.
• Scopus CiteScore: 9.1 (journalsearches.com).
• h-index: 44 (Resurchify / journalsearches.com).
• SCImago Journal Rank (SJR): 1.196, with the journal ranked in Q1 in both Artificial Intelligence and Computer Science Applications categories; overall rank ~3,657 (Resurchify).

Official metrics: For the authoritative current Impact Factor, Eigenfactor, and Article Influence scores, consult the journal's IEEE Xplore bibliometrics panel, which draws directly from Clarivate JCR.

Notable Papers and Special Issues

Despite its young age, IEEE TAI has published several widely referenced papers. The examples below are drawn from third-party citation trackers (researchtrend.ai) and institutional repositories; citation figures vary across databases, so they are given as indicative.

• "Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations" — Mingfu Xue, Yushu Zhang, Jian Wang, and Weiqiang Liu (2020). One of the inaugural volume's papers, this taxonomy of neural-network watermarking and IP protection is among the journal's most-cited early articles (third-party trackers report well over 300 citations).
• "Graph Learning: A Survey" — Xiwei Xu, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu (2021). A comprehensive survey of graph neural networks and graph representation learning, reported by third-party trackers with several hundred citations.
• "Fostering Human Rights in Responsible AI: A Systematic Review for Best Practices in Industry" — Baldassarre, Caivano, Fernandez Nieto, Gigante, and Ragone (2024), TAI 6(2), pp. 416–431, DOI: 10.1109/TAI.2024.3394389. An example of the journal's openness to AI ethics and societal-impact research.
• "A Temporal-Spatial Graph Network with a Learnable Adjacency Matrix for Appliance-Level Electricity Consumption Prediction" — Li, Xia, Xiao, and Shi (2025), TAI 6(4), pp. 989–1002. Representative of the journal's applied-AI portfolio (ISSN 2691-4581 confirmed on the article record).

The journal has also run special issues on topics including AI ethics ("Artificial Intelligence Ethics: Technical Solutions and Data-driven Guidelines," guest-edited by Francesca Rossi, Keeley Crockett, Lirong Xia, and Matt Garratt), security and privacy in machine learning, sentiment analysis as a multidisciplinary research area, and AI methods for maintenance and safety of automation systems. Submission deadlines for those calls have closed, but the calls illustrate the journal's thematic reach.

Key Facts at a Glance

Frequently Asked Questions

What is IEEE Transactions on Artificial Intelligence?

IEEE Transactions on Artificial Intelligence (IEEE TAI) is a peer-reviewed monthly journal launched by IEEE in 2020, publishing theories, methodologies, and applications of artificial intelligence. It is managed by the IEEE Computational Intelligence Society on behalf of five IEEE societies.

What is the ISSN of IEEE Transactions on Artificial Intelligence?

The electronic ISSN is 2691-4581, confirmed by multiple university research portals and institutional repositories.

Who is the Editor-in-Chief of IEEE TAI?

The 2025 editorial board published by the IEEE Computational Intelligence Society lists Prof. Jun Wang (Chair Professor of Computational Intelligence, City University of Hong Kong) as Editor-in-Chief. The Founding Editor-in-Chief (2020) was Prof. Hussein Abbass of UNSW Canberra, Australia.

Is IEEE Transactions on Artificial Intelligence open access?

IEEE's official title list classifies it as Hybrid Open Access: authors may publish traditionally or pay an article processing charge to make their article freely available. A TAI-specific APC figure was not officially published on the journal's author pages at the time of writing — confirm the current fee through the IEEE Author Portal or submission site.

How do I submit a paper to IEEE TAI?

Submit through the IEEE Author Portal at ieee.atyponrex.com/journal/tai-ieee. Use the IEEE paper templates from template-selector.ieee.org, remove all author-identifying information (double-anonymous review), and ensure the similarity index stays at or below 20%.

Is the journal indexed in Scopus and Web of Science?

Journal-metric aggregators report Scopus coverage from 2020 through 2026. The journal's IEEE Xplore bibliometrics panel is populated from Clarivate's Journal Citation Reports, indicating JCR-based metrics exist. Authors should verify the latest indexing status directly in Scopus and the Web of Science Master Journal List before citing it in promotion or funding documents.

What are the impact factor and CiteScore of IEEE TAI?

No official consolidated figure was published on the journal's own pages. Third-party aggregators report: impact score ~6.07 (2025, Scopus-based, Resurchify), Scopus CiteScore 9.1, h-index 44, and SJR 1.196 (Q1 in Artificial Intelligence and Computer Science Applications). Treat these as indicative; the authoritative numbers are the JCR-based metrics shown in IEEE Xplore.

How long does peer review take?

IEEE TAI does not publish an official average review time. Its review-process documentation describes a compliance check, plagiarism screening, and double-anonymous peer review by at least two reviewers. Unofficial third-party reviews suggest decisions typically take several months, but this is not officially verified.

When was IEEE TAI founded?

It was announced in March 2020, opened submissions in April 2020, and published its inaugural issue in August 2020 — three issues were planned for the launch year, and the journal has since grown to monthly publication.