Introduction

Vincent Zhong and Artur Jaschke

Introduction to Artistic Intelligence and Artificial Intelligence (AI & AI)

The contemporary convergence of artificial intelligence and what scholars increasingly call artistic intelligence represents one of the most profound socio-technical realignments of the twenty-first century. It demands a rigorous examination across disciplinary, institutional, and cultural boundaries and therefore a potential paradigm shift of how we view both 'AI' systems. 

Artificial intelligence, broadly conceived as the development of computational systems capable of performing tasks that traditionally required human cognition, has undergone a paradigmatic shift from rule-based symbolic processing to data-driven generative architectures, thereby acquiring capacities for pattern synthesis, stylistic emulation, and autonomous content generation that closely mirror, and in certain domains surpass, established benchmarks of creative production. Artistic intelligence, by contrast, denotes the distinctly human constellation of aesthetic reasoning, contextual interpretation, emotional resonance, and intentional expression that has historically underpinned cultural creation. It is not merely a technical competency but an embodied, culturally situated mode of meaning-making that integrates perceptual sensitivity, historical awareness, and normative judgement. The intersection of these two forms of intelligence therefore transcends mere technological augmentation, constituting instead a complex epistemic and practical nexus wherein algorithmic generation and human creativity co-evolve, challenge conventional ontologies of authorship, and recalibrate the boundaries between tool, collaborator and autonomous agent. This conceptual recalibration demands scrutiny of how computational systems encode aesthetic priors, as the latent spaces of modern neural architectures do not merely replicate existing cultural forms but actively synthesise novel configurations that challenge established taxonomies of genre, medium and creative intentionality. Consequently, the boundary between emulation and innovation becomes increasingly difficult to demarcate without robust theoretical scaffolding that accounts for both statistical probability and culturally contingent meaning. 

Within contemporary society, this convergence manifests as both an unprecedented democratisation of creative tools and a source of profound normative uncertainty, as generative systems increasingly mediate public discourse, reshape cultural consumption patterns, and provoke urgent debates regarding authenticity, intellectual provenance and the ethical governance of machine-produced artefacts. The rapid deployment of large-scale generative models has, consequently, compelled policymakers, educators and civil society organisations to re-evaluate frameworks for cultural literacy, digital citizenship and the preservation of human-centric creative ecosystems. Furthermore, the sociological dimension of this shift encompasses the transformation of audience perception, wherein machine-generated outputs are no longer evaluated solely through traditional critical frameworks but are increasingly assessed through interactive, participatory and algorithmically mediated feedback mechanisms that blur the distinction between creator, curator and consumer. This recalibration of cultural reception necessitates empirical investigation into how trust, attribution and aesthetic appreciation are cognitively processed when the provenance of creative material remains partially or wholly obscured. In the realm of scientific and scholarly research, the dynamics of artificial and artistic intelligence have catalysed a methodological reorientation towards interdisciplinary praxis, drawing upon computational creativity, cognitive neuroscience, human–computer interaction and digital humanities to develop more nuanced evaluation paradigms that move beyond superficial performance metrics and instead interrogate the underlying mechanisms of novelty, coherence, aesthetic value and contextual appropriateness. Researchers are increasingly required to design mixed-methods studies that couple quantitative analysis of model outputs with qualitative assessments of human reception, while simultaneously addressing reproducibility challenges, dataset biases and the epistemic opacity of black-box generative architectures. 

Concurrently, the economic implications of this intersection are equally transformative, as creative industries spanning visual arts, music, literature, design and entertainment undergo structural reconfiguration driven by automation, algorithmic curation, and new forms of intellectual property contention. The traditional labour dynamics of creative work are being unsettled by the displacement of certain routine artistic tasks, the emergence of hybrid roles requiring technical literacy alongside creative judgement, and the formation of novel value chains in which data provenance, model training rights and output attribution constitute contested economic assets. The commercialisation of these technologies also introduces complex valuation paradoxes, as the marginal cost of digital content generation approaches zero while the premium placed on verifiable human authorship, provenance certification and culturally resonant narratives simultaneously escalates, thereby generating asymmetric market dynamics that require novel economic modelling and policy intervention to prevent concentration of creative capital and ensure sustainable livelihoods for practitioners across the artistic spectrum. 

Moreover, the macroeconomic impact extends beyond individual enterprises to national innovation strategies, as governments and institutional funders increasingly recognise creative AI as a strategic sector capable of generating exportable intellectual capital, stimulating regional cultural economies, and enhancing soft power through digitally mediated cultural diplomacy. However, this potential remains contingent upon the establishment of transparent regulatory environments, equitable access to computational infrastructure, and robust frameworks for fair remuneration and rights management. The interplay between artificial and artistic intelligence thus cannot be adequately understood through a purely technological lens, nor can it be reduced to a zero-sum narrative of human obsolescence; rather, it necessitates a systemic analysis that accounts for the recursive feedback loops between algorithmic development, cultural practice, institutional adaptation and market dynamics, while remaining attentive to the historical contingencies that shape how societies assign value, legitimacy, and meaning to creative labour. As digital platforms continue to integrate generative capabilities into everyday creative workflows, the distinction between human-originated and machine-assisted artefacts grows increasingly porous, prompting a fundamental re-examination of what constitutes originality, how aesthetic authority is negotiated, and which aspects of creative intelligence remain irreducibly human. These questions are not merely philosophical abstractions but pressing empirical concerns that inform curriculum design, intellectual property legislation, algorithmic auditing practices, and the ethical deployment of autonomous creative systems in public and commercial spheres. This chapter therefore situates the intersection of artificial and artistic intelligence within a broader analytical framework that bridges technical innovation, cultural theory and socio-economic policy, aiming to provide readers with a comprehensive foundation for understanding how these convergent intelligences are reshaping contemporary research agendas, redefining creative labour markets, and reconfiguring the cultural infrastructure of modern societies, while also identifying critical knowledge gaps and methodological priorities that will determine the trajectory of this evolving field in the decades ahead.

Since this digital book is dedicated to Artistic Intelligence and Artificial Intelligence (AI & AI), what do we actually mean by 'Artificial' and by 'Artistic,' and how should we view the notion of an abstract conception of ‘intelligence' in this context?

Based on various sources, ‘intelligence’ is generally described as the ‘ability’ to ‘understand,’ ‘process’ information and knowledge, and ‘think,' react to, ‘engage in,' and ‘deal with’ the ‘environment.’ [1] The scholars Shane Legg and Marcus Hutter made a valuable collection of definitions of ‘intelligence’ where they adopted the following overarching definition:  ‘Intelligence measures an agent’s ability to achieve goals in a wide range of environments.’ [2] In this publication, we argue that intelligence functions between interfaces: between humans; between human beings and surroundings; between non-human elements; as well as between the arts and our society and the world. 

Extending to ‘artistic’ and ‘artificial,' therefore, we believe it’s logical to establish an understanding that Artistic Intelligence claims intelligence based on human creativity, while Artificial Intelligence assumes intelligence from a mechanical perspective. 

The proposition that Artistic Intelligence derives its legitimacy from human creativity while Artificial Intelligence operates from a mechanical paradigm presents an initially seductive dichotomy, one that neatly partitions the domain of human expression from the realm of computational processing. At first glance, this distinction appears ontologically secure: artistic cognition is traditionally understood as an embodied, affective and culturally situated phenomenon, emerging from the subjective interplay of memory, emotion and lived experience, whereas artificial cognition is fundamentally syntactic, governed by algorithmic architectures, statistical inference and optimised data pathways devoid of phenomenological interiority. Yet to accept this bifurcation as exhaustive is to overlook the profound epistemological and empirical convergences that have begun to dissolve the boundaries between these categories. Contemporary computational systems, particularly generative neural networks, no longer merely replicate mechanical calculation; they engage in pattern synthesis, latent space navigation and stochastic recombination that yield outputs indistinguishable in form and function from human-generated art, music and literature. 

Simultaneously, cognitive science has increasingly revealed that human creativity itself is not an ineffable spark of inspiration but a highly structured cognitive process rooted in associative memory, predictive modelling and neurobiological feedback loops that operate with striking algorithmic regularity. The brain, after all, is a biological machine, and the aesthetic judgments we attribute to artistic intelligence are increasingly understood as emergent properties of evolutionary heuristics, cultural conditioning and hierarchical predictive processing. This reciprocal revelation forces a reconsideration of what intelligence actually entails when decoupled from anthropocentric assumptions. If creativity can be formalised as the novel recombination of existing information within constrained parameters, then both human and artificial systems are engaged in fundamentally similar operations, differentiated primarily by substrate, scale and the presence or absence of conscious self-awareness. The philosophical tension thus shifts from a question of mechanical versus organic origin to a deeper inquiry into the nature of agency, intentionality and the ontology of creation. When an algorithm produces a composition that evokes profound emotional resonance, does the absence of subjective experience diminish its artistic status, or does it merely reframe our understanding of how meaning is generated and received? 

Searle’s Chinese Room argument once suggested that syntax cannot give rise to semantics, yet the collaborative dynamics between human curators and machine generators demonstrate that meaning is increasingly co-constructed, emerging not from isolated cognition but from interactive ecosystems where human intention and machine iteration continuously shape one another. Furthermore, the mechanical perspective itself is not devoid of creative potential. Emergence, a cornerstone of complex systems theory, demonstrates that novel behaviours and structures routinely arise from simple rule-based interactions without top-down design, suggesting that what we label as mechanical may simply be a different ontological register of the same generative principles that govern biological creativity. Such a recalibration requires abandoning the lingering Cartesian impulse to separate mind from mechanism, recognising that both biological and synthetic networks operate through iterative optimisation, environmental feedback and structural plasticity. When we acknowledge that aesthetic judgment relies on statistical expectation as much as emotional intuition, the supposed divide between human inspiration and algorithmic production reveals itself as an artifact of outdated dualism. The future of intelligence studies must embrace a post-dualist framework that treats creativity as a distributed phenomenon, irreducible to any single substrate but manifest in the relational dynamics among observer, artifact and generative system. The distinction, therefore, cannot be maintained as a rigid categorical boundary but must be reconceptualised as a continuum of cognitive architectures, each operating along axes of autonomy, adaptability and contextual embedding. To claim that artistic intelligence is exclusively human is to ignore the historical contingency of aesthetic frameworks, while asserting that artificial intelligence is merely mechanical underestimates the transformative capacity of recursive learning and self-organising systems. 

Consequently, the inquiry transcends mere classification and becomes a fundamental reexamination of how consciousness, creativity and computational logic intersect in an increasingly entangled epistemic landscape. Ultimately, the question of whether the distinction is simple dissolves under scrutiny into a more nuanced recognition that intelligence, in all its forms, is a dynamic process of pattern recognition, contextual adaptation and meaning generation, whether instantiated in wetware or silicon. The real philosophical and scientific challenge lies not in policing the boundaries between the artistic and the artificial but in developing a unified epistemology that accounts for the pluralistic nature of cognition, acknowledges the co-evolutionary trajectory of human and machine creativity, and redefines artistic intelligence not as a monopoly of biological consciousness but as an emergent property of any system capable of transforming information into novel, resonant configurations. In this light, the dichotomy collapses not into equivalence, but into a richer, more intricate understanding of how intelligence manifests across different substrates, urging us to move beyond binary classifications and toward a pluralistic ontology of creative cognition that honours both the embodied depth of human experience and the generative potential of computational architectures. This paradigmatic evolution ultimately compels us to recognise that intelligence is neither strictly organic nor purely synthetic, but rather a spectrum of adaptive information processing wherein creativity emerges as a function of complexity, constraint, and contextual feedback rather than biological privilege alone.

On another dimension, we asked two simple yet profound questions to two of the most used AI systems at the moment, ChatGPT and Claude: 

a) What is artistic intelligence, and 

b) What is the relation between artistic intelligence and artificial intelligence?

Below is how they responded to question a:

ChatGPT:

‘Artistic intelligence is the ability to create, interpret, and express ideas, emotions, or meaning through artistic forms like visual art, music, writing, dance, or design.  It’s not just about being 'good at art'—it’s about how someone thinks and perceives the world creatively.’  

Claude:

‘Artistic intelligence is the capacity to perceive, interpret, and express meaning through aesthetic form. It's one of the most distinctly human kinds of intelligence — and one of the hardest to define — because it operates across cognitive, emotional, and sensory registers simultaneously.’

To question b:

ChatGPT:

‘The relationship between artistic intelligence and artificial intelligence (AI) is really about human creativity meeting machine capability. They overlap more and more today—but they’re not the same thing.

Core difference (in one line)

  • Artistic intelligence = human ability to create meaning and express emotion 
  • Artificial intelligence = machines simulating intelligent behaviour using data and algorithms’

Claude:

‘What they have in common

Both involve pattern recognition at scale. A painter who has looked at ten thousand paintings, a musician who has internalized a tradition, a poet steeped in the canon — they are, in a functional sense, doing something structurally similar to what a large language model or image generator does: absorbing vast patterns and recombining them in response to a prompt (internal or external). This parallel is uncomfortable for some, illuminating for others.

Both also operate in the space between rules and deviation. Art isn't random — it works with and against conventions. AI systems similarly navigate learned structure while generating novel outputs. The tension is the same, even if the mechanism is entirely different.

Where they diverge

The deepest difference is interiority. Artistic intelligence in humans is rooted in experience — suffering, longing, joy, mortality, the specific texture of a life. When Coltrane plays, something personal is at stake. When a generative model produces a saxophone line, nothing is at stake for the model. The output may be indistinguishable; the origin is not.

There's also the question of intention. Human artists make choices that are motivated — by something they want to say, resist, or discover. AI systems optimize toward outputs that satisfy a prompt. These can look identical from the outside while being categorically different from the inside.’

For the record, we don’t impose judgement on the above responses while merely using them as a trigger. Both Claude and ChatGPT tend to describe Artistic intelligence from human-rooted perspectives while presenting Artificial intelligence in the non-human scope. In the meantime, they do suggest the relationships between these two AIs are intrinsic. 

However, are the boundaries between these two AIs still that clear anymore, or are there even boundaries in between?

The above question served as the starting point of the project ‘AI & AI,' where the survey, workshop, two sub-projects, the video essay, and discussions that are included in this digital book began to grow.

What Was the Project About?

Artificial Intelligence was no longer a question about ‘to be’ or ‘not to be'; it’s more realistic to consider it as a ‘how’ question since AI has been permeating more and more aspects of our society and daily life, and more importantly, there seems to be no way back. In the arts, AI had not been something novel for quite a while either. Discussions were happening in different groups with diverse opinions. Some of our students and colleagues had been exploring, experimenting or actively working with AI, in various ways and contexts. Some were holding their reservations or hesitations about AI, and there were also fears and resentment. At the professorship Music-based Therapies and Interventions (MTI), we have been looking into, among others, the intersection of AI and music-based therapies and interventions, cultural diversity and societal impact for quite some time. In the meantime, we were very interested in what our students and colleagues were thinking about AI and how they were (or were not) working with AI, in the context of education and research, as well as how they saw the future with AI. Thus, we took a step further and brought the discussion about AI into the landscape of the arts, as well as education and research, starting from the AI & AI project.

What Was the Journey of the Project?

AI & AI Survey

In the second half of 2024, we designed and launched an AI & AI survey for the ArtEZ University of the Arts community, including students and staff members. We aimed to form a picture of the general atmosphere and usage of AI in the ArtEZ community. The survey included questions about whether and which AI/AI-driven technologies they used, reasons why they used or didn’t use AI, their general feelings about AI and reasons, and future perspectives towards AI, among others. We received responses from a wide range of disciplines, backgrounds and roles, which firmly underpinned the development of valuable understandings of the AI ‘vibes’ in/around the ArtEZ community back then. The responses depicted quite a polarised picture, in which resistance and advocacy, anxiety and hope, cautious attempts and deep involvements, concerns and prospects towards AI co-existed.

AI & AI Workshop

Following the insights gained from the survey, we organised an AI & AI workshop on November 29, 2024, in Deventer. The participants who signed up were from diverse backgrounds and roles, including students, researchers, teachers and other staff members from different disciplines and departments. Artur Jaschke and Vincent Zhong, on behalf of the professorship MTI, hosted the workshop. In addition, an artist-observer role was set up to observe alongside the workshop and then create an artwork based on the observation and reflection during the process. The artist LI Yuchen was invited to participate, and she subsequently created a video essay.

The workshop consisted of chapters of Prelude, Sparks, Rehearsal, Talkshow, Sharing, and Prospect. The lector of MTI, Dr Jaschke, opened the workshop with a presentation ‘Revolutionising Therapy: AI-Powered Therapeutic Solutions-Bringing Accessible, Affordable, and Personalised Mental Health Care to Everyone.’ In the presentation, Jaschke introduced the AI therapy tool and ethical considerations, and shared insights into music and art therapy integration, as well as the use of AI in education, among others.

Afterwards, the setting of ‘talkshow’ was introduced to the workshop, where the participants were guided to group themselves based on the chapter of Sparks (when they had the chance to share their stories around AI and get to know each other), and co-design a ‘talkshow’ following the storyline as: a guest/interviewee is launching a new work/product (from their chosen practice/field) named ‘Art Intelligence’ that’s related to AI, and joining the ‘talkshow’ for publicity purposes. Afterwards, the group members were to fit into four roles: 

  • MC: the host of the ‘talkshow,’ who is supposed to lead the conversation and stay neutral about AI and its impact on the arts.
  • Guest/interviewee, who is expected to showcase their work/product ‘Art Intelligence’ and ideas/stories behind the work.
  • Journalist: who is to write a critical piece (in the role, not in reality) about the work/product, and who holds a critical attitude towards AI.
  • Audience member: a big fan of AI who therefore strives to promote it. 

Each group chose and established their scenario, story, and discussions, and eventually presented their ‘talkshow’ around a central question: Do you and/or how do you centre human in the making/creation process (how do you take ethics, cultures and/or policies into account as well) of the work/product ‘Art Intelligence’? Discussions were sparked by various ideas and essential points, for instance, societal impact and consequences of AI, a possible AI-powered wealth redistribution system, legal frameworks around AI, AI-driven knowledge production and utilisation, and AI in education, among others. Not only did the participants present fascinating ‘talkshows,' the diverse perspectives and fresh ideas brought to the discussions were invaluable to the workshop, the project, and also the dialogue at large.

During the workshop, arrangements for the next steps of the project were officially communicated as well. A call for proposals was announced, which aimed to provide opportunities, including budget and supervision, for students to apply for, design and execute their projects to explore AI in their studies, research, and/or practices. The outcomes of the student-led sub-projects, together with the artwork created by the artist observer, were to be included in the AI & AI digital book to showcase the project journey as well as the sparkles, insights and outcomes from it.

AI & AI Video Essay

Before the workshop, together with Yuchen, we had constructive conversations regarding the project, the intention, her role, the workshop, and the possible outcome. We intended to allow as much space and freedom as possible for Yuchen to participate, to observe, to reflect, to create, and eventually to provide her perspective on AI, from an artist’s eyes and heart. A beautiful and frank video essay, ‘Cosmic Conversation Partner,’ was delivered later, as you can see in this digital book. In the video essay, Yuchen tapped into probabilities and possibilities of human-AI relationships through an ‘epistolary form.’ [3] You are invited to click and watch, and share your thoughts on the very topical human-AI relationality.

AI & AI Student-Led Sub-Projects

Evgeniia van Zonneveld and Ricardo Sulzle, two ArtEZ students (at the time of the workshop), pitched to the call for proposals and were later invited to conduct their projects and research.

On the sub-project that Ricardo leads, Machinic Analyst, the question of how AI sees and interprets humans is centred, and a fascinating experiment was implemented in which AI was used to process and analyse data and information from a workflow through facial recognition software and a Rorschach inkblot. Another valuable layer added is that AI regulation and ‘AI sycophancy’ [4]  are touched upon, as well as in Ricardo’s reflection. Through the supervision talks, Ricardo shared the evolution of his expectations and design for the project, his curiosity about AI, challenges, and also his effort to bring vibrant ideas to the ground and then build it up step by step from there.

On the other sub-project led by Evgeniia, Cyborg ID, in collaboration with Aleksandr Markov and Sofiia Oliinyk, an AI platform was designed and invites users to create ‘digital identities’ in a constraint- and judgement-free space. Analysis of and experiments with ‘identity’ and ‘fluidity’ were encouraged and empowered by the project and the creators. In the consultations with Evgeniia, we discussed everything from her artistic goals to the struggle for the optimisation of project management, from the positioning of AI in her practice to technical realisation. A well-thought-out and implemented project was delivered through beautiful teamwork. 

Besides the inspiring and uplifting creativity and effort that the students and their teams have put in, we feel privileged and delighted to have witnessed the adventurous research journey they have been on, through the supervision and conversations we had together. Exquisite ideas, hard work, struggles, challenges, self-development, solutions, and eventually fruitful outcomes mark the journey a unique and meaningful experience for the students and their teams, but also for us, in various dimensions. 

AI & AI Digital Book

We wanted to take it a step further and make an effort to produce this AI & AI digital book for multiple reasons. It has been a rewarding and fruitful experience for us to work with students, colleagues, external experts and critical friends on this particular topic in diverse ways and formats. We hoped this book could help capture, document, and showcase some parts of the meaningful journey. In the meantime, we aimed to create and facilitate a platform through the digital book for students and artists to showcase their inspiring works to the world, as well as to hopefully contribute to the further development of their studies, research, and/or careers.

This book might mark the end of the current phase of the AI & AI project. However, we hold the hope that this could serve as a starting point to nurture more ideas around this topic and spark more meaningful dialogues and discussions among an even larger audience.

We feel deeply grateful for having had the opportunity to work with the students and their teams, the artist observer, and all the other participants and partners who have been part of the marvellous journey, and with whom we have all the way reached the publication together. We would also like to express our appreciation to ArtEZ Press, the design team, and other facilitators for the pleasant collaborations on the book. It is our sincere hope that readers will enjoy it and be inspired as much as we have been!

Bibliography

  • Cambridge Advanced Learner’s Dictionary, Cambridge University Press, 2026.
  • Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., and Jurafsky, D., ‘Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence.’ arXiv, arXiv:2510.01395. 2025.
  • Legg, S., and Hutter, M., ‘A Collection of Definitions of Intelligence.’ In B. Goertzel and P. Wang, eds., Advances in Artificial General Intelligence 157 (2007): pp. 17–24. IOS Press BV.
  • LI, Yuchen, Director. Cosmic Conversation Partner. Motion Picture, 2025.
  • Nakashima, H., ‘AI as Complex Information Processing.’ Minds and Machines 9 (1999): pp. 57–80.
  • Neisser, U., Boodoo, G., Thomas J. Bouchard Jr., Boykin, A. W., Brody, N., Ceci, S. J., et al., ‘Intelligence: Knowns and Unknowns.’ American Psychologist 51.2 (1996): pp. 77–101.
  • Sternberg, R. J., (ed.), The Cambridge Handbook of Intelligence. 2nd ed. Cambridge University Press, 2020.
  • Wechsler, D., The Measurement and Appraisal of Adult Intelligence. 4th ed. Williams & Wilkins Co, 1958.
  • Word Central Student Dictionary. 2006.

Footnotes

  1. U. Neisser et al., ‘Intelligence: Knowns and Unknowns,’ American Psychologist 51, no. 2 (1996): pp. 77–101; Cambridge Advance Learner’s Dictionary (Cambridge University Press, 2026); H. Nakashima, ‘AI as Complex Information Processing’, Minds and Machines 9 (1999): pp. 57–80; D. Wechsler, The Measurement and Appraisal of Adult Intelligence. 4th ed. (Williams & Wilkins Co, 1958); Word Central Student Dictionary (2006); R. J. Sternberg, ed., The Cambridge Handbook of Intelligence. 2nd ed. (Cambridge University Press, 2020).
  2.  S. Legg and M. Hutter, ‘A Collection of Definitions of Intelligence,’ in B. Goertzel and P. Wang, eds., Advances in Artificial General Intelligence 157 (IOS Press BV, 2007): pp. 17–24.
  3.  LI 2025.
  4.  M. Cheng, C. Lee, P. Khadpe, S. Yu, D. Han, and D. Jurafsky, ‘Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence,’ arXiv, arXiv:2510.01395 (2025).