Abstract
This article is an invitation to discuss a recurrent question in the field of Humanities: “Does it make any sense to talk about Humanities in the age of generative AI?”. While many papers have been devoted to this topic, this paper brings together three distinct disciplinary viewpoints, namely the humanities, computer science, and education, to examine it from complementary angles. It discusses labor market transformations, ethical issues related to authorship and plagiarism, cultural bias, as well as the effects of AI on students' cognitive processes and learning practices. It also considers how these issues are being reshaped in current expert discussions. From these three perspectives, the analysis suggests that profound changes and transformations are inevitable, and the Humanities are no exception. In this context, it becomes necessary to distinguish between those tasks that cannot be replaced by artificial intelligence and those that are likely to be progressively taken over by it.
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Keywords: Artificial Intelligence (AI), humanities, digital transformation, ethical challenges, education
1. INTRODUCTION
The impact of AI on our lives has been widely debated across disciplines such as philosophy, sociology, education, science, etc. While each field offers its own perspective, what happens when three individuals from very different backgrounds engage in a discussion together?
Following a presentation by one of the authors at the KU-KADH International Conference on Digital Humanities 2025 at Korea University, the three authors, from different backgrounds, began a discussion on the impact of AI on the humanities. Among the various topics considered, the discussion focused on its impact on the humanities labor market, ethical issues related to authorship and plagiarism, and the digitalization of education, particularly in South Korea.
This paper presents a concise account of our discussion. While a substantial body of research has addressed these issues from various perspectives, this study offers a complementary viewpoint by identifying common ground among three professionals engaged in AI and the humanities. This multi-perspective approach is intended to contribute to ongoing debates and suggest new directions for future research.
This paper is organized into two parts. The first part (Sections 3, 4, and 5) provides a review of current discussions on AI and the humanities, including its impact on the humanities, education, and the Korean context. This section serves to situate the present study while offering an overview of relevant literature. Although it is not intended to be exhaustive, it outlines a concise yet comprehensive set of key questions that have been previously raised in the field.
The second part (Section 6, Discussion) presents the main ideas that emerged from the exchange among the three authors. This section is not intended to be exhaustive or systematic; rather, it offers a focused selection of points that reflect their shared perspective and contribute additional elements to ongoing discussions, some of which may provide novel insights.
The paper concludes with a final section outlining our perspective on the future of the humanities in the era of AI. While this view may appear optimistic, it underscores the continuing relevance and necessity of the humanities, as discussed throughout the study.
2. BACKGROUND
The rapid development of artificial intelligence (AI) has expanded its presence into domains traditionally associated with human cognition and creativity. In particular, advances in natural language processing and generative models have enabled AI systems to produce texts, images, and other forms of content with increasing sophistication (
Bartlett et al. 2025). As a result, activities once considered uniquely human, such as interpretation, authorship, and creative expression, are now partially mediated by computational systems, raising questions about the boundaries between human and machine-generated knowledge.
AI offers new possibilities for research, communication, and creativity, but also introduces challenges. This suggests that AI should not be understood as a neutral tool, but as a force that reshapes the conditions under which knowledge is produced and interpreted, redefining professional practices, authorship, and the role of human-centered skills.
At the same time, digitalization has reshaped education, altering teaching practices and learning experiences (
Savva et al. 2022). The increasing use of digital tools, online platforms, and AI-based systems has created new forms of access and efficiency, while also raising concerns about attention, comprehension, and the development of cognitive skills. As digital technologies become more deeply embedded in education, the relationship between technology, learning, and knowledge acquisition has become a central topic of debate.
The following sections examine how AI is transforming the Humanities and its educational contexts, focusing on its impact on professional practices, ethical frameworks, and learning processes.
3. IMPACT OF AI ON THE HUMANITIES
3.1 Marketplace
The rapid development of large language models (LLMs) and other AI technologies is reconfiguring the humanities labor market. In contrast to previous technological shifts, the occupations most affected are not manual or physical jobs, but knowledge-intensive professions. Within the Humanities, these include authors, translators, interpreters, proofreaders, and other forms of text-based work traditionally associated with humanities graduates. In this context, 'knowledge-intensive' refers not to tasks involving large-scale information processing, but to those requiring the production and manipulation of written language and routine information handling.
Recent studies provide empirical support for this transformation.
Eloundou et al. (2024) report high levels of exposure among humanities-related professions, including interpreters and translators (76%), survey researchers (75%), and poets, lyricists, and creative writers (68.8%). Additional evidence from
Research.com (2026) similarly identifies archivists, library technicians, editors, and proofreaders as highly susceptible to automation.
At the same time, these analyses point to areas that remain less affected by AI, such as critical interpretation, creative expression, teaching and mentoring, interpersonal communication, and context-dependent decision-making. These developments suggest that the key distinction is not technological, but cognitive. It depends on how tasks are performed, not just what tools are used.
The impact of AI is particularly evident in the translation industry, where neural machine translation tools such as DeepL and ChatGPT increasingly perform tasks previously carried out by human professionals. Activities such as literary and corporate translation, interpretation, and dubbing are among those most exposed to automation (
AbroadLink 2026). Rather than complete replacement, however, these developments suggest a shift in professional roles, with greater emphasis on editing, supervision, and quality control of AI-generated outputs (Multi-Lingual 2025). We argue that this transformation redefines expertise in the humanities, shifting it from content production toward the supervision, interpretation, and evaluation of AI-generated material.
Alongside its transformative potential, AI poses significant ethical challenges for the Humanities, particularly in relation to plagiarism, impersonation, and the redefinition of authorship. AI systems are increasingly used to compose, organize, and edit texts, making it difficult to determine the boundary between plagiarism and AI-assisted writing. The concept of “AI-giarism,” introduced by
Chan (2023), highlights this ambiguity, showing that students tend to disapprove of direct AI-generated content while adopting more permissive attitudes toward indirect or assisted uses.
The distinction between acceptable and unacceptable uses of AI is therefore becoming increasingly blurred. More fundamentally, AI destabilizes the concept of authorship itself, rather than merely complicating its regulation. These tensions are further intensified by differences in how students and instructors perceive AI use.
Galindo-Domínguez et al. (2025) argue that the widespread use of AI contributes to the normalization of practices previously considered plagiarism, as AI becomes integrated into all stages of the learning process.
A related concern is the difficulty of reliably detecting AI-generated text. Despite the development of increasingly sophisticated detection tools, their performance remains inconsistent, often producing both false positives and false negatives. This raises important ethical concerns, including the risk of unjust accusations as well as the acceptance of undetected academic misconduct (
Hiett 2025).
These challenges extend beyond the academic context into the creative industries. AI systems, trained on large corpora of human-produced texts, are capable of generating outputs that closely resemble existing styles and themes, making it difficult to distinguish between original and AI-generated content. Organizations such as the
Authors Guild (2025) have raised concerns about the long-term implications of these developments, particularly regarding the sustainability of professional writing and creative work. Ongoing legal debates over whether the use of copyrighted material in AI training constitutes fair use further illustrate the complexity of these issues.
4. IMPACT ON EDUCATION
4.1 Cognitive and Behavioral Effects
The increasing integration of digital technologies into educational environments is bringing about significant changes in students' cognitive and behavioral patterns. While technology itself is not inherently detrimental, its widespread and often uncritical use in education has raised concerns about its effects on attention, comprehension, and learning. Several studies point to a decline in sustained attention and deep reading, as digital environments tend to encourage fragmented and rapid forms of information consumption.
One commonly discussed issue is the so-called digital native fallacy, which assumes that younger generations, simply because they are familiar with digital devices, possess advanced cognitive and learning skills. However, empirical evidence shows that frequent exposure to digital media does not necessarily improve academic ability. Students may be skilled at producing and consuming content, for example, creating short videos or engaging on social media, while lacking essential skills such as evaluating information, verifying sources, and organizing knowledge.
Social media further reinforces these patterns. Platforms built around short, high-frequency content contribute to shorter attention spans and more fragmented thinking. Research in cognitive neuroscience and social psychology links this to changes in reward systems driven by dopamine feedback, which promote habitual use and reduce the ability to concentrate for extended periods. As a result, students may struggle to maintain focus, engage in complex reasoning, and complete tasks that require sustained effort. Multitasking also proves ineffective, as attention shifts between tasks instead of processing them simultaneously, reducing overall efficiency (
Haidt 2024).
These changes are linked to broader shifts in cognitive functioning, including lower reading comprehension, reduced vocabulary development, and weaker analytical skills. Importantly, these trends are no longer limited to early education but are increasingly visible among university students. Although outcomes vary across regions, international assessments such as PISA indicate that these challenges are widespread, even in relatively strong educational systems.
4.2 Pedagogical and Learning Issues
Alongside broader cognitive shifts, the digitization of education has led to significant transformations in pedagogical practices and learning environments. Digital tools are often introduced without corresponding changes in teaching methods, leading to a superficial use of technology in the classroom. For example, tablets and other devices are typically used to display content rather than to support active engagement or critical thinking (
Livingstone 2012).
These shifts have been accompanied by a gradual lowering of academic demands, which may affect students’ ability to sustain attention and engage with cognitively demanding tasks (
Firth et al. 2019). To accommodate changing performance levels, educational materials have become more simplified and visually oriented, reducing the emphasis on extended reading and analytical writing. At the same time, the role of teachers has changed, with increasing challenges to traditional forms of authority and classroom management in digitally mediated environments.
A related issue concerns how students interact with learning materials. Research shows that reading on paper supports better comprehension and retention than reading on digital screens, partly due to the spatial and tactile stability of printed texts. Similarly, handwriting has been linked to improved learning outcomes, as it engages fine motor processes and reinforces cognitive encoding. These findings suggest that replacing traditional practices with purely digital alternatives may have unintended effects on learning effectiveness (
Mangen et al. 2013).
This shows that the issue is not only the tools themselves, but how they are reshaping the way students learn. Shifts in reading and writing practices also affect how information is understood and retained. When learning becomes more fragmented or less physically engaged, it may be harder to develop the sustained attention required for complex material. This is especially relevant for the Humanities, where learning depends on close reading, interpretation, and the ability to follow extended arguments.
The increasing presence of digital devices has also influenced students’ social and emotional development. Reduced face-to-face interaction and greater reliance on mediated communication have been linked to difficulties in emotional expression and interpersonal engagement. Concerns have also been raised about a reduced emotional vocabulary and a more limited capacity for emotional regulation among younger generations. Humanities-based practices, such as literary reading and interpretative engagement with texts, can support the development of emotional awareness and empathy by encouraging perspective-taking and reflection on human experience (
Kidd et al. 2013). These practices involve forms of engagement that are not easily replicated in fully digital environments.
Digitization should therefore not be equated with educational innovation. Digital technologies are better understood as tools whose value depends on how they are used within specific pedagogical contexts (
Castañeda and Selwyn 2018). This raises questions about whether current approaches to digital integration fit the cognitive and pedagogical demands of humanities education.
These developments suggest that humanities education must shift its focus from content production toward the critical evaluation, interpretation, and revision of AI-generated material.
4.3 Potential Benefits of Implementing AI in the Classroom
While the preceding sections have highlighted the risks associated with digitalization and AI, these developments also create new possibilities for expanding the scope and methods of Humanities education. AI can be understood not only in terms of limitation or disruption, but also as a tool that enables new forms of interaction with knowledge and text (
Chun and Elkins, 2023).
In particular, when it comes to educational technology, AI-supported systems facilitate more individualized and responsive learning environments (
Zawacki-Richter et al. 2019). Personalized learning technologies allow instructional content to be adapted to different levels of proficiency (
Maghsudi et al. 2021), while AI-based tutoring systems provide immediate feedback that supports sustained engagement with complex materials (
Graesser et al. 2004). These functions help restructure learning processes in ways that promote deeper interaction with texts rather than passive consumption.
Moreover, such technologies enable new forms of analysis and interpretation that align with the goals of Digital Humanities. Tools for text analysis, pattern recognition, and large-scale data processing may allow students to engage with textual materials in more creative and different ways. On the other hand, they may also allow to expand the range of methodologies in Humanities education.
5. AI AND HUMANITIES IN THE KOREAN CONTEXT
5.1 Institutional and Research Transformation
The Korean context illustrates the broader transformation discussed above, particularly the shift from knowledge production toward the evaluation and organization of AI-mediated content. In this setting, AI redefines the relationship between knowledge, institutions, and humanities practices through the development of state-led digital humanities infrastructures. Closely tied to national digital frameworks, these initiatives reveal how AI-driven changes unfold within institutional contexts. Early efforts focused on the large-scale digitization of cultural heritage and archival materials, supported by government-led projects since the late 1990s (
Lee and Lee, 2019). Rather than simply expanding traditional humanities practices, this case shows how AI shifts the focus toward managing, organizing, and interpreting large-scale digital knowledge systems.
Although often described as an extension of digitization, the Korean case suggests a more fundamental shift. Rather than simply converting materials into digital form, AI reconfigures how knowledge is organized, interpreted, and institutionally embedded within a state-driven technological regime. In this sense, AI is changing how knowledge is produced and organized.
Recent studies further indicate that AI is transforming digital knowledge systems, including scholarly communication, information retrieval, and metadata organization (
Peng and Li 2025). These developments reshape how knowledge circulates within academic systems and how it is accessed and legitimized.
At the same time, the adoption of digital methodologies within humanities disciplines remains uneven. The resulting “digital/humanities divide” reflects a misalignment between technological infrastructure and disciplinary practice, in which extensive digitization has not translated into corresponding methodological change (
Cha 2015). In Korea, digitization has been driven primarily by government initiatives rather than by the needs of humanities scholars, limiting the integration of these infrastructures into everyday research practice.
This misalignment can be attributed to multiple factors. Institutional evaluation systems continue to prioritize traditional forms of scholarship, while disciplinary cultures favor interpretive reading over computational approaches. Furthermore, the top-down nature of digitization initiatives has limited the alignment between technological tools and the practical and intellectual needs of humanities research. More fundamentally, we argue that the core issue lies in a deeper disconnect between technological implementation and the ways knowledge is produced and interpreted within the humanities.
Within this context, the introduction of generative AI intensifies existing tensions. As AI systems become integrated into research processes such as text production, summarization, and evaluation (
Kim & Noh 2025), the role of human judgment in academic work becomes increasingly uncertain, raising questions about the epistemic authority of interpretation, authorship, and evaluation. At the same time, humanities research in Korea continues to rely largely on interpretive approaches applied to digitized data rather than on computational methods (
Lee and Lee 2019). AI therefore operates alongside existing practices, reshaping their conditions without fully displacing them.
This dynamic reinforces the central argument of this study: AI-driven change in the humanities is not merely technological, but structurally embedded in institutional and policy frameworks. Addressing this divide requires more than expanding technological infrastructure; it calls for reconfiguring evaluation criteria and integrating interdisciplinary training within humanities education and research. As AI increasingly reshapes how knowledge is evaluated, organized, and legitimized, these institutional adjustments become essential.
5.2 Ethical and Cultural Dimensions
The Korean context further illustrates the ethical and cultural dimensions of AI integration, particularly in relation to authorship, cultural creation, and governance. The increasing use of AI in text generation and creative work complicates traditional notions of originality and intellectual ownership. These issues are especially significant within Korea’s highly developed cultural industries, where digital media plays a central role in both national identity and global cultural exchange (
Jin 2024).
AI redefines the role of human creativity in cultural production, functioning not only as a technical tool but also as a cultural agent that influences narratives and representations, making the boundary between human and machine-generated content increasingly difficult to define.
At the same time, South Korea has developed policy frameworks and ethical guidelines to address these challenges. Efforts to establish trustworthy AI reflect a broader regional shift from soft regulatory approaches toward more formalized governance structures (
Xu et al. 2024). These frameworks are formed through interactions among government, academia, industry, and civil society, reflecting a multistakeholder approach in which public values are negotiated rather than imposed (
Ha 2022).
This dynamic is evident in public debates surrounding biased AI systems, such as the Lee Luda chatbot case, which exposed the social risks of AI and highlighted the limitations of existing regulatory frameworks, reinforcing the need for context-sensitive ethical standards and demonstrating how AI in the Humanities is closely tied to broader questions of cultural representation, institutional responsibility, and the governance of emerging technologies. In this regard, the Korean context demonstrates in practice how ethical and cultural challenges emerge from the interaction between technological systems and institutional conditions.
5.3 Educational and Pedagogical Implications
In the Korean context, the integration of AI into education has been particularly rapid, reflecting national priorities in digital transformation and technological innovation. AI-based learning platforms, automated assessment tools, and data-driven educational systems are increasingly incorporated into both secondary and higher education, transforming how knowledge is delivered and evaluated (
Ministry of Education, Korea 2023).
Such developments align with the potential benefits of AI discussed in Section 4, particularly in terms of personalized learning and enhanced accessibility. AI-supported systems facilitate more individualized learning environments by tailoring instructional content to different levels of proficiency and providing immediate feedback (
Zawacki-Richter et al. 2019). Empirical studies in Korean higher education also show that such approaches contribute to measurable improvements in students’ AI literacy (
Kim and Kim 2026). These patterns are also reflected in studies of Korean higher education, which report both increased learning efficiency and concerns about output quality and reliability in AI-supported environments (
Oh 2025).
However, these changes also raise concerns regarding the depth of learning, particularly in contexts where efficiency-driven approaches are emphasized (
OECD 2021, 2023). In the Korean educational context, where academic competition and standardized evaluation remain prominent, the use of AI may reinforce efficiency-driven approaches to learning, potentially at the expense of critical and interpretative engagement.
In practice, this dynamic is reflected in changes to both student and instructor behavior. Students increasingly rely on AI tools to generate initial drafts, summarize readings, and refine arguments, which can reduce time spent on sustained reading and independent writing. In response, instructors are redesigning assignments to emphasize process over product, incorporating iterative drafts, in-class writing, and oral examinations, while also reconsidering evaluation criteria to focus on critical engagement, originality, and the ability to assess AI-generated content. These developments reflect broader discussions on the need to redesign learning and assessment in the AI era, with greater emphasis on higher-order thinking and process-oriented evaluation (
Oh 2025;
Park and Jeon 2026). As a result, the focus of learning shifts from knowledge production toward the critical assessment, interpretation, and refinement of AI-generated outputs.
This tension is particularly significant for Humanities education. While AI expands access to information, it also challenges pedagogical practices centered on sustained reading, analytical writing, and interpretative reasoning. The integration of AI therefore requires careful alignment with pedagogical frameworks that support sustained attention, analytical writing, and interpretative reasoning.
At the same time, AI introduces new possibilities aligned with the goals of Digital Humanities. In Humanities courses, AI tools are incorporated into activities such as text analysis and interpretative tasks, enabling students to combine close reading with computational approaches. This process typically involves generating initial analyses, comparing alternative interpretations, and critically revising AI-generated outputs through guided instruction.
Overall, the Korean educational context illustrates how AI introduces both opportunities and constraints, reinforcing that its impact depends on how it is embedded within existing institutional and pedagogical frameworks. More broadly, the Korean case demonstrates that these transformations are shaped not by technology alone, but by the institutional and policy conditions that structure how AI is implemented and used.
6. DISCUSSION
This study examines the impact of artificial intelligence on the Humanities across professional, ethical, and educational domains. These changes unfold differently across tasks, knowledge practices, and learning environments, while remaining closely interconnected.
In addressing these issues, the discussion raises a set of questions that are relevant to the humanities, academia, and educational institutions. The first question emerges directly from this context:
Would we recommend a student to enroll in a Humanities studies program?
In the labor market, the impact of AI on the humanities can be understood as a structural differentiation between (a) automatable tasks and (b) irreducibly human activities. The first category includes routine language-based tasks such as translation, summarization, and editing, which are increasingly automated as tools become more precise and efficient. These developments suggest that such tasks may be largely overtaken by AI, with human involvement primarily limited to supervision.
By contrast, activities such as interpretation, teaching, and communication continue to rely on human judgment and contextual awareness. Whether these domains will also be substantially transformed by AI remains an open question. It is premature to offer a definitive answer, although some of these activities are already becoming AI-assisted, particularly in educational contexts. A central premise underlying this discussion is that AI operates through the imitation of human behavior. The extent to which these activities may be affected therefore depends on how effectively AI can approximate human performance. While current systems are not yet indistinguishable from humans, this possibility raises important concerns for the future.
Overall, these developments point to a transformation that goes beyond job displacement, as they redefine what constitutes expertise in the humanities labor market. Adapting to AI is not just about learning technical skills. It also requires stronger interpretative and contextual abilities such as interpretation, critical judgment, and contextual reasoning. The challenge is not to match AI in efficiency, but to sustain and develop forms of work that remain fundamentally human. This leads to the next question:
What is “fundamentally” human?
This question leads into a philosophical domain that extends beyond the immediate scope of this article. However, a shared position emerges from the discussion: artistic creation can be understood as a fundamentally human activity. While AI is capable of imitating human expression, it remains limited in its ability to produce what may be considered “original.” In this context, the term original is understood in the sense defined by the Oxford Dictionary as something “created personally by a particular artist, writer, or musician; not a copy.”
A similar distinction applies to art appreciation. Although AI can simulate evaluative responses by reproducing existing interpretations, it does not possess the capacity to generate original engagement with artistic works. For this reason, art appreciation remains grounded in human experience. By extension, domains such as ethical reasoning, philosophy, cultural interpretation in a broad sense, and historical inquiry continue to rely on forms of judgment that are closely tied to human capacities.
Beyond current technological limitations in creativity, an additional concern emerges regarding whether such domains should be delegated to AI at all. This consideration leads to the next question:
Can we trust AI?
From an ethical and cultural perspective, the use of AI raises fundamental questions about authorship, originality, and cultural representation. In particular, it complicates the distinction between human and machine authorship, rendering traditional notions of originality increasingly unstable.
Concerns about reliability arise not only from the technical limitations of AI systems, but also from the nature of the data on which they are trained. The predominance of English-language, Western-centric datasets introduces cultural and ideological biases into AI outputs, raising the risk of marginalizing non-Western perspectives.
As reliance on AI technologies increases, these issues extend beyond bias to broader questions of cultural sovereignty and linguistic diversity. This is particularly relevant for societies that lack the resources to develop AI systems aligned with their own linguistic and cultural contexts.
These considerations suggest that the use of AI may require certain forms of limitation or regulation:
Should AI be detectable? And controlled?
The preceding discussion suggests that the ethical challenges posed by AI cannot be addressed through detection tools or regulation alone. What is at stake is not merely control, but the ways in which authorship and originality are understood when human and machine contributions become intertwined. In this context, humanities scholarship plays a central role in developing the conceptual frameworks needed to respond to these changes. Without such frameworks, the distinction between acceptable and unacceptable uses of AI becomes increasingly difficult to sustain.
This raises the question of who or what should be responsible for detecting and controlling AI. Current detection tools remain limited in accuracy, and there is reason to assume that improvements in detection will be accompanied by corresponding advances in AI systems, making them progressively more difficult to identify. A similar dynamic can be observed in computer security, where increasing system robustness is matched by increasingly sophisticated forms of attack.
Despite these challenges, human judgment still appears capable, in many cases, of distinguishing between human-produced and AI-generated texts, particularly in the domain of creative writing. As long as a recognizable “human touch” remains perceptible, the possibility of identifying and regulating AI use may persist.
A related concern involves the integration of AI into education. The growing capacity of AI systems to collect, organize, and present information creates strong incentives for their widespread use. However, certain intellectual capacities must be developed independently of their immediate utility. Even if AI systems can perform tasks such as information retrieval and organization, these skills remain essential to intellectual formation. The comparison with basic arithmetic is instructive: although calculators are widely used, the ability to perform calculations manually continues to be regarded as fundamental.
This leads to the next question:
What should be the role of AI in education?
In education, AI expands access, improves efficiency, and supports personalized learning. However, its uncritical use may lead to reduced attention, superficial learning, and shifts in pedagogical priorities. In response, educational practices are increasingly moving toward process- based approaches that emphasize sustained reading, analytical writing, and critical engagement with AI-generated content.
In this context, AI should not be understood merely as a technological addition to existing practices, but as a force that reconfigures how knowledge is accessed, analyzed, and interpreted.
There is growing evidence that students are becoming accustomed to fast and fragmented interaction with content, making it more difficult to sustain attention on longer and more complex material. This has direct implications for the humanities, where learning depends on careful reading, interpretation, and the ability to follow extended arguments. If these tendencies persist, the cognitive foundations that support humanities learning may gradually weaken.
In such an environment, the ability to critically evaluate and interpret information becomes more important than the ability to produce it, particularly as AI systems increasingly generate content on behalf of students.
As illustrated by the Korean context, these transformations are closely linked to institutional and policy-driven dynamics rather than technological change alone. AI therefore operates within broader institutional systems that shape how knowledge is produced, evaluated, and transmitted in the humanities.
The impact of AI ultimately depends on the extent to which tasks require interpretation, contextual understanding, and human judgment. While this perspective helps explain how AI reshapes the humanities, it remains grounded in a conceptual and context-specific analysis rather than broad empirical evidence. Further research is therefore needed to examine these dynamics across different cultural and institutional settings, and to explore how AI literacy can be integrated into humanities education while sustaining core human-centered capacities such as sustained reading, critical reasoning, and creative expression.
7. CONCLUSIONS
The discussion surrounding AI is endless. This study has focused on a limited set of issues, approached from distinct yet complementary perspectives. Rather than displacing the humanities, AI appears to be reconfiguring them, shifting the emphasis from content production toward interpretation, critical judgment, and contextual understanding. This has specially happened in engineering areas, most remarkably in software engineering, where menial work (formerly perceived as creative, like writing code) is being taken over by AI systems. A comparable development may be expected in the humanities.
In an environment where information can be generated instantly and at scale, the distinction between producing knowledge and understanding it becomes increasingly significant. The humanities occupy a critical position within this shift, not because they generate more information, but because they provide the frameworks to interpret, evaluate, and assign meaning to it. This is the human perspective that, from our point of view, cannot be replaced by AI.
In this sense, the future of the Humanities does not depend on resisting technological change, but on demonstrating their continued relevance in domains that require interpretation and reasoning about human experience.
This position can be summarized as follows: “As long as humans are distinguishable from AI, humanities will continue to exist”.
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