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Emerging Research in Computational Creativity and Creative Robotics
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Emerging Research in Computational Creativity and Creative Robotics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 6800

Special Issue Editors

Dr. Sanaz Nikghadam-Hojjati

E-Mail Website
Guest Editor
CTS-UNINOVA, LASI, NOVA University Lisbon, CNR-ISTI, 2829-517 Caparica, Portugal
Interests: computational creativity; business intelligence; open innovation
Prof. Dr. José Barata

E-Mail Website
Guest Editor
CTS-UNINOVA, LASI, NOVA University Lisbon, 2829-517 Caparica, Portugal
Interests: intelligent manufacturing; agent-based manufacturing; cyber-physical systems; evolvable production system
Special Issues, Collections and Topics in MDPI journals
Dr. Eda Marchetti

E-Mail Website
Guest Editor
Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo”, Area della Ricerca CNR di Pisa, Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: access control; model-based specification and testing; security and privacy testing and assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current polycrisis, volatile, uncertain, complex, and ambiguous (VUCA) world, traditional tools that could once be used to successfully address isolated needs are no longer sufficient. There is a growing demand for creative solutions that can deliver multidimensional value while addressing interconnected and evolving challenges. Within this context, Computational Creativity and Creative Robotics are emerging as vital interdisciplinary research fields that aim to push the boundaries of machine intelligence and human–machine collaboration. These fields bring together insights from information processing, Artificial Intelligence (AI), Human–Machine Interaction (HMI), Affective Computing, Cognitive science, Philosophy and Aesthetics, Neuroscience, Design, Art, Cybersecurity, and Ethics to develop novel approaches that simulate, augment, and interact with creative processes. Increasingly, systems are being designed to generate art, music, stories, designs, and even behaviours, as well as to work collaboratively with humans in various creative and expressive domains. At the same time, the integration of embodied agents such as robots capable of sensing, interpreting, and physically engaging with their environment is ushering in new opportunities for creativity in physical space.

This Special Issue aims to bring together cutting-edge research and practical developments that explore the theoretical, technical, and applied dimensions of computational creativity and creative robotics. We invite contributions that investigate or demonstrate how artificial and robotic systems can model, support, engage in, or enhance creativity. Submissions may include original research, theoretical analysis, system architectures, case studies, and application-based innovations.

Prospective authors are cordially invited to submit their original manuscripts on topics including, but not limited to, the following:

  • Creative algorithms and generative models;
  • Embodied creative agents and robotic systems;
  • Co-Creative Human–AI/Robotic frameworks;
  • Information architectures for creativity;
  • Knowledge representation and reasoning in creative systems;
  • Evaluation metrics for artificial creativity;
  • Cognitive modelling of creative behaviour;
  • Human–robot creative interaction and affective engagement;
  • Cybersecure and human-centred Creative Robotics Agents/Computational Systems;
  • Computational creativity and creative robotics applications in design, digital art, education, entertainment, and manufacturing.

Dr. Sanaz Nikghadam-Hojjati
Prof. Dr. José Barata
Dr. Eda Marchetti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational creativity
  • creative robotics
  • generative models
  • human–machine interaction
  • co-creative systems
  • cybersecurity
  • human-centred creative systems
  • artificial intelligence in creativity
  • embodied creative agents
  • affective computing
  • knowledge representation for creativity
  • information-driven innovation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

22 pages, 570 KB  
Article
Machines Prefer Humans as Literary Authors: Evaluating Authorship Bias in Large Language Models
by Marco Rospocher, Massimo Salgaro and Simone Rebora
Information 2026, 17(1), 95; https://doi.org/10.3390/info17010095 - 16 Jan 2026
Viewed by 326
Abstract
Automata and artificial intelligence (AI) have long occupied a central place in cultural and artistic imagination, and the recent proliferation of AI-generated artworks has intensified debates about authorship, creativity, and human agency. Empirical studies show that audiences often perceive AI-generated works as less [...] Read more.
Automata and artificial intelligence (AI) have long occupied a central place in cultural and artistic imagination, and the recent proliferation of AI-generated artworks has intensified debates about authorship, creativity, and human agency. Empirical studies show that audiences often perceive AI-generated works as less authentic or emotionally resonant than human creations, with authorship attribution strongly shaping esthetic judgments. Yet little attention has been paid to how AI systems themselves evaluate creative authorship. This study investigates how large language models (LLMs) evaluate literary quality under different framings of authorship—Human, AI, or Human+AI collaboration. Using a questionnaire-based experimental design, we prompted four instruction-tuned LLMs (ChatGPT 4, Gemini 2, Gemma 3, and LLaMA 3) to read and assess three short stories in Italian, originally generated by ChatGPT 4 in the narrative style of Roald Dahl. For each story × authorship condition × model combination, we collected 100 questionnaire completions, yielding 3600 responses in total. Across esthetic, literary, and inclusiveness dimensions, the stated authorship systematically conditioned model judgments: identical stories were consistently rated more favorably when framed as human-authored or human–AI co-authored than when labeled as AI-authored, revealing a robust negative bias toward AI authorship. Model-specific analyses further indicate distinctive evaluative profiles and inclusiveness thresholds across proprietary and open-source systems. Our findings extend research on attribution bias into the computational realm, showing that LLM-based evaluations reproduce human-like assumptions about creative agency and literary value. We publicly release all materials to facilitate transparency and future comparative work on AI-mediated literary evaluation. Full article
(This article belongs to the Special Issue Emerging Research in Computational Creativity and Creative Robotics)
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23 pages, 3312 KB  
Article
Service Mode Switching for Autonomous Robots and Small Intelligent Vehicles Using Pedestrian Personality Categorization and Flow Series Fluctuation
by Peimin Zhang, Wanwan Hu, Lusheng Wang, Hai Lin, Weiping Li and Min Peng
Information 2026, 17(1), 43; https://doi.org/10.3390/info17010043 - 4 Jan 2026
Viewed by 231
Abstract
Autonomous robots and small intelligent vehicles with diverse service functions have been extensively researched and are expected to be deployed in scenarios such as sci-tech parks, museums, and transportation hubs. Although designed as AI-driven assistants, they may not always provide optimal customer service. [...] Read more.
Autonomous robots and small intelligent vehicles with diverse service functions have been extensively researched and are expected to be deployed in scenarios such as sci-tech parks, museums, and transportation hubs. Although designed as AI-driven assistants, they may not always provide optimal customer service. A key challenge is achieving service intelligence, where adaptive mode switching plays a critical role. Our experimental research demonstrates that the composition of pedestrian types can be inferred from microscopic flow fluctuations. This finding enables the development of effective service mode switching strategies. Therefore, this article proposes a method that classifies pedestrians by their temperament-based behaviors, simulates their movement, and extracts microscopic features from flow data using moving standard deviation (MSTD) and moving root mean square (MRMS) indicators. Analysis of these features enables inference of approximate composition ratio of different pedestrian types, consequently enabling a targeted switching mechanism between active and passive service modes. Simulations confirm that each pedestrian type exhibits distinct flow patterns, and the employed indicators can effectively estimate pedestrian ratios through microscopic flow data analysis, thereby facilitating efficient service mode switching. Furthermore, validation using pedestrian flow data extracted from real-world video footage confirms the method’s applicability and effectiveness. Full article
(This article belongs to the Special Issue Emerging Research in Computational Creativity and Creative Robotics)
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19 pages, 286 KB  
Article
Designing Co-Creative Systems: Five Paradoxes in Human–AI Collaboration
by Zainab Salma, Raquel Hijón-Neira and Celeste Pizarro
Information 2025, 16(10), 909; https://doi.org/10.3390/info16100909 - 17 Oct 2025
Cited by 2 | Viewed by 5604
Abstract
The rapid integration of generative artificial intelligence (AI) into creative workflows is transforming design from a human-driven activity into a synergistic process between humans and AI systems. Yet, most current tools still operate as linear “executors” of user commands, which fundamentally clashes with [...] Read more.
The rapid integration of generative artificial intelligence (AI) into creative workflows is transforming design from a human-driven activity into a synergistic process between humans and AI systems. Yet, most current tools still operate as linear “executors” of user commands, which fundamentally clashes with the non-linear, iterative, and ambiguous nature of human creativity. Addressing this gap, this article introduces a conceptual framework of five irreducible paradoxes—ambiguity vs. precision, control vs. serendipity, speed vs. reflection, individual vs. collective, and originality vs. remix—as core design tensions that shape human–AI co-creative systems. Rather than treating these tensions as problems to solve, we argue they should be understood as design drivers that can guide the creation of next-generation co-creative environments. Through a critical synthesis of existing literature, we show how current executor-based AI tools (e.g., Microsoft 365 Copilot, Midjourney) fail to support non-linear exploration, refinement, and human creative agency. This study contributes a novel theoretical lens for critically analyzing existing systems and a generative framework for designing human–AI collaboration environments that augment, rather than replace, human creative agency. Full article
(This article belongs to the Special Issue Emerging Research in Computational Creativity and Creative Robotics)
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