Revisiting Skopos Theory in the Era of AI: A Theoretical Analysis of Translation Purpose and Machine Mediation

Skopos Theory, as a foundational framework in functionalist translation studies, emerged in the late 1970s within the German school of translation theory, marking a pivotal departure from the linguistic-centric approaches that had dominated the field for much of the 20th century. Prior to its development, translation scholarship was largely anchored in equivalence theory, which prioritized the formal or semantic alignment between source and target texts, often treating translation as a static transfer of linguistic elements rather than a purpose-driven communicative act. This linguistic focus, however, struggled to account for real-world translation scenarios where texts served diverse social, cultural, or practical functions—such as advertising slogans, technical manuals, or literary adaptations—where strict equivalence might undermine the text’s intended impact. It was against this backdrop that scholars like Hans J. Vermeer and Katharina Reiss sought to reorient translation theory toward function and context, laying the groundwork for Skopos Theory as a response to the limitations of equivalence-based models.

At the core of Skopos Theory lies the skopos rule, which posits that the purpose of the translation (its skopos, derived from the Greek word for “goal” or “purpose”) determines the translation strategy and methods employed. Unlike equivalence theory, which centers the source text as the primary reference point, Skopos Theory shifts the focus to the target context: the translation’s intended audience, the communicative situation in which it will be received, and the specific function it is meant to fulfill in that context. This rule is complemented by two secondary principles that refine its application: the coherence rule and the fidelity rule. The coherence rule requires that the translated text be intelligible and contextually appropriate for its target audience, ensuring it functions as a meaningful communicative act within the target culture’s linguistic and cultural framework. The fidelity rule, in turn, mandates a reasonable degree of intertextual connection between the source and target texts, though this connection is not fixed—it is contingent on the translation’s skopos. For example, a technical manual translated for a non-expert audience might prioritize clarity and simplicity over literal adherence to the source text’s jargon, while a literary translation aiming to preserve the author’s stylistic voice might lean closer to formal equivalence.

Beyond these core rules, Skopos Theory introduces the concept of the “translator’s commission,” which refers to the explicit or implicit instructions guiding the translation process, often provided by clients or stakeholders. This commission encapsulates the skopos and contextual constraints, empowering the translator to make strategic decisions that align with the intended purpose rather than being bound by rigid linguistic norms. This emphasis on the translator’s agency is a defining feature of the theory: it frames the translator not as a passive “transferor” of language, but as a skilled communicator who mediates between source and target contexts to achieve a specific goal.

The historical significance of Skopos Theory lies in its paradigm shift from text-centered to context-centered translation. By prioritizing function over form, it expanded the scope of translation studies to encompass the social, cultural, and pragmatic dimensions of translation, making it relevant to professional contexts where translation serves tangible, real-world needs. For instance, in localization—where products or services are adapted for global markets—Skopos Theory provides a theoretical basis for modifying content to resonate with local cultural values or consumer behaviors, a practice that equivalence models cannot adequately explain. Similarly, in audiovisual translation, such as subtitling or dubbing, the theory justifies adjustments to dialogue length or cultural references to ensure the content is accessible and engaging for target audiences. Even in literary translation, Skopos Theory offers a framework for justifying adaptations: a children’s edition of a classic novel, for example, might simplify complex narratives or remove mature themes to align with its skopos of entertaining and educating young readers.

In summary, Skopos Theory’s core tenets redefine translation as a purposeful, context-dependent activity, while its historical context reflects a broader shift in translation studies toward functionalism and practical relevance. By centering the target text’s function and the translator’s agency, it provides a flexible, actionable framework that bridges theoretical inquiry and professional practice, making it a cornerstone of modern translation scholarship and a critical tool for addressing the diverse challenges of real-world translation.

Machine mediation in AI translation refers to the integration of machine translation (MT) systems as non-human intermediaries in the translation process, reshaping the traditional dynamics between translators, source texts, and target texts. At its core, this reconfiguration hinges on a redefined division of labor between humans and machines, where each party assumes tasks aligned with their respective strengths: MT systems handle the rapid processing of lexical and syntactic transfers based on pre-trained algorithms, while human translators focus on contextual adaptation, cultural nuance, and purpose-driven refinement. This division is operationalized through structured workflows such as pre-editing, MT processing, and post-editing—pre-editing involves human translators optimizing source texts for MT compatibility by clarifying ambiguous syntax or removing domain-specific jargon that algorithms may misinterpret, post-editing entails revising MT output to align with the target text’s skopos (purpose), and algorithmic decision-making in translation choices occurs when MT systems prioritize certain lexical equivalents or sentence structures based on statistical frequency or neural network predictions, which humans then validate or adjust.

This division of labor drives a fundamental shift in the traditional translator role, moving from the sole author of the target text to a mediator or curator of machine output. Historically, translators were responsible for every stage of text production, from decoding the source text’s meaning to encoding it into the target language while balancing linguistic accuracy and cultural appropriateness. In AI-mediated workflows, however, translators no longer initiate the target text from scratch; instead, they inherit a machine-generated draft and engage in curatorial practices: they evaluate the draft’s alignment with the skopos (e.g., whether a marketing text’s persuasive tone is preserved), retain effective algorithmic choices, and revise segments where machine output fails to capture contextual subtleties—for example, a neural MT system might translate a colloquial idiom literally, and the translator would rephrase it to resonate with the target audience’s cultural context. As mediators, translators also bridge the gap between algorithmic limitations and human communicative needs: they interpret how MT systems’ training data biases (e.g., overrepresentation of formal texts) may skew output, and adjust the draft to ensure inclusivity or domain specificity.

Within Skopos Theory’s framework, machine mediation also reshapes concepts of translator invisibility and responsibility. Translator invisibility, traditionally defined as the tendency to prioritize target text fluency over visible translator intervention, undergoes a paradoxical transformation: while MT output may initially appear “invisible” (masking the machine’s role as a producer), the translator’s curatorial work becomes more visible through deliberate revisions that highlight human judgment. For instance, a post-edited legal text may bear traces of the translator’s adjustments to MT-generated legal terminology, signaling human oversight rather than seamless machine production. Concurrently, translator responsibility expands beyond the target text’s linguistic accuracy to include accountability for algorithmic choices: under Skopos Theory, the translator remains the ultimate authority over whether the target text achieves its intended purpose, so they must take responsibility for validating MT decisions—if an algorithm selects a culturally insensitive term, the translator’s failure to revise it constitutes a breach of skopos-aligned responsibility. This expanded responsibility also involves curating the MT system itself: translators may provide feedback on algorithmic errors to improve future output, embedding their judgment into the system’s learning process and blurring the line between human agency and machine functionality.

The reconfiguration of translator agency through machine mediation is of practical importance because it optimizes translation efficiency while preserving the human-centric focus of Skopos Theory. By leveraging MT for routine tasks, translators can allocate more time to purpose-driven decision-making, which is critical for high-stakes texts (e.g., medical instructions or diplomatic documents) where skopos alignment directly impacts real-world outcomes. Furthermore, this reconfiguration challenges Skopos Theory to evolve its conceptualization of agency: instead of framing agency as individual human autonomy, it now encompasses the collaborative agency between humans and machines, where translators guide algorithms to serve the target text’s skopos. This evolution ensures Skopos Theory remains relevant in the AI era, as it acknowledges that effective translation no longer relies solely on human expertise but on the synergistic integration of human judgment and machine efficiency.

AI’s integration into translation practices redefines the skopos as a dynamically negotiated construct rather than a static, human-defined objective, a shift that challenges Skopos Theory’s traditional emphasis on a single, pre-determined purpose set by human actors. At its core, this dynamic negotiation arises from the interplay between explicit skopos articulated by human stakeholders—such as clients specifying a target audience (e.g., medical professionals requiring terminological precision) or translators prioritizing cultural adaptability—and implicit skopos embedded within AI systems, shaped by their training data biases and optimization metrics. These implicit skopos are not intentionally designed but emerge from the technical constraints and data-driven logics that govern AI behavior, creating a tension that demands ongoing reconciliation between human intent and algorithmic predisposition.

A key driver of this negotiation is the influence of training data biases on AI’s implicit purpose. For instance, an AI translation model trained primarily on formal diplomatic texts may implicitly prioritize syntactic formality and literal accuracy, even when a client’s explicit skopos calls for conversational fluency for a consumer-facing marketing campaign. Here, the AI’s implicit purpose—rooted in the distribution of genres, registers, and cultural norms in its training corpus—conflicts with the client’s stated goal, requiring translators to mediate by adjusting prompts, fine-tuning the model with domain-specific data, or post-editing outputs to align algorithmic outputs with human intent. Similarly, training data skewed toward Western cultural contexts may lead an AI to prioritize idiomatic expressions familiar to English-speaking audiences, even when a client’s skopos demands localization for non-Western markets; this discrepancy forces stakeholders to negotiate between the AI’s culturally biased implicit purpose and the client’s explicit goal of cross-cultural resonance.

Optimization metrics further complicate this negotiation by embedding competing implicit purposes within AI systems. Most commercial AI models balance fluency and accuracy as core optimization targets, but the weighting of these metrics varies by developer: some prioritize fluency to enhance readability, while others prioritize accuracy to minimize terminological errors. This variation creates scenarios where an AI’s implicit purpose—shaped by its developer’s metric choices—clashes with human-defined skopos. Consider a legal translation project where a client’s explicit skopos requires 100% terminological accuracy to avoid contractual disputes; if the AI is optimized for fluency, it may paraphrase legal jargon to improve readability, undermining the client’s purpose. In such cases, translators must engage in iterative negotiation: they may adjust the model’s temperature setting to reduce creative paraphrasing, supplement prompts with terminological glossaries, or manually correct errors to reconcile the AI’s fluency-focused implicit purpose with the client’s accuracy-driven explicit goal.

This co-construction of skopos challenges Skopos Theory’s traditional framework, which posits a single, human-defined purpose as the guiding principle of translation. In traditional Skopos Theory, the skopos is a fixed anchor, with all translation decisions subordinated to this pre-determined objective. However, AI introduces a multiplicity of purposes—explicit from humans, implicit from algorithms—that cannot be reduced to a single, unified skopos. For example, in a medical translation scenario, a client’s explicit skopos may be to ensure regulatory compliance through precise terminology, while the AI’s implicit purpose (shaped by optimization for fluency) may prioritize natural-sounding prose over terminological rigor, and the translator’s professional purpose may involve balancing both to avoid clinical misinterpretation. Here, the final skopos is not set by any single actor but emerges from a recursive negotiation process: the translator refines prompts to emphasize accuracy, the client provides additional context about regulatory requirements, and the AI’s outputs are iteratively adjusted until the three stakeholders’ purposes converge toward a co-constructed objective.

This dynamic negotiation also highlights the limitations of Skopos Theory’s failure to account for non-human agents in purpose-setting. The theory’s original formulation assumes that humans alone define and execute the skopos, but AI’s implicit purposes—rooted in technical and data-driven logics—act as independent variables that shape translation outcomes. For instance, an AI optimized for speed may prioritize rapid output generation, even when a client’s skopos demands meticulous attention to cultural nuance; this tension requires stakeholders to negotiate trade-offs between efficiency (the AI’s implicit purpose) and cultural adaptability (the client’s explicit purpose). In such cases, the skopos is not a fixed target but a fluid construct that evolves through ongoing interaction between human intent and algorithmic behavior, forcing a rethinking of Skopos Theory’s core premise of a single, human-centric purpose.

Ultimately, AI’s role in translation transforms the skopos into a collaborative, negotiated outcome rather than a unilateral human decision. This shift does not negate Skopos Theory’s value but expands its scope to recognize the agency of non-human actors in purpose formation. By acknowledging the dynamic interplay between explicit human skopos and implicit algorithmic skopos, translation practice moves beyond static purpose-setting to embrace a more inclusive framework that accounts for the technical, data-driven logics of AI, while still centering human judgment in mediating and reconciling competing purposes to achieve functional, contextually appropriate translation outcomes.

Norm adaptation in AI translation refers to the dynamic process by which translators or AI systems adjust to the intersection of source-culture norms, target-culture expectations, and machine-generated norms—patterns embedded in AI models through training on global multilingual datasets. Source-culture norms encompass context-dependent linguistic conventions, cultural references, and rhetorical strategies that shape the source text’s meaning and communicative intent; for example, Chinese idioms like “画蛇添足” (adding legs to a snake) carry metaphorical connotations of redundant action rooted in ancient fables, while Japanese honorific language (keigo) encodes social hierarchy and interpersonal distance as core cultural values. In contrast, machine-generated norms emerge from the statistical patterns of AI training corpora, which often prioritize linguistic standardization, semantic neutrality, and global intelligibility over culture-specific nuance. These norms are further reinforced by algorithmic preferences: transformer-based models like GPT-4 or DeepL tend to favor high-frequency collocations and syntactic structures that maximize cross-lingual alignment, even if this comes at the cost of erasing culture-bound meaning.

The tension between these two sets of norms directly impacts the coherence rule and fidelity rule of Skopos theory, which together govern the target text’s functional validity. The coherence rule requires the target text to be communicatively coherent within its target context, meaning it must align with the target audience’s cultural knowledge and interpretive frameworks. However, machine-generated norms often disrupt this coherence by flattening source-culture specificity into generic expressions. For instance, when translating the Indian Hindi phrase “अकेला चना भाड़ नहीं फोड़ सकता” (a single chickpea cannot split a pot) into English, AI systems frequently render it as “one person cannot do it alone”—a semantically accurate but culturally denuded version. While this translation is intelligible to global English speakers, it fails to resonate with the target audience in a localized Indian English context, where the original’s agricultural imagery evokes collective labor values central to rural communities. Here, the machine’s preference for standardization undermines the coherence rule by stripping the text of the cultural resonance needed to engage the intended audience.

Concurrently, the fidelity rule—mandating the target text’s alignment with the source text’s intent—faces strain when machine-generated norms prioritize statistical plausibility over semantic or pragmatic fidelity. Consider the translation of a Korean corporate press release that uses the honorific suffix -nim to address a retiring executive, a marker of respect tied to Korean workplace hierarchy. AI systems often omit this suffix in English translations, as English lacks a direct grammatical equivalent and the training data underrepresents honorific-dependent contexts. While the resulting translation is grammatically correct, it erases the source text’s intent to convey deference, violating the fidelity rule by misaligning the target text’s interpersonal tone with the source’s communicative purpose.

A more nuanced case study involves the translation of African American Vernacular English (AAVE) poetry into standard English. AAVE’s use of copula deletion (e.g., “she tired” instead of “she is tired”) and cultural metaphors (e.g., “soul food” as a symbol of community and heritage) are core to its poetic identity. AI models, trained on predominantly standard English corpora, often “correct” copula deletion and replace culture-specific metaphors with generic alternatives—for example, translating “soul food feeds the spirit” as “comfort food nourishes the soul.” This not only distorts the poet’s stylistic intent (violating the fidelity rule) but also alienates the target audience of AAVE poetry enthusiasts, who rely on these linguistic features to interpret the work’s cultural and emotional layers (undermining the coherence rule).

These tensions reveal a critical paradox: while AI translation excels at efficiency and global intelligibility, its machine-generated norms often clash with the context-dependent demands of Skopos theory’s coherence and fidelity rules. Resolving this paradox requires translators to act as norm mediators, leveraging AI’s efficiency while manually adjusting for culture-specific nuance—for example, retaining the agricultural imagery in the Hindi chickpea metaphor with a parenthetical explanation for non-local audiences, or adding a footnote to the Korean press release to clarify the honorific context. In doing so, translators reaffirm Skopos theory’s core premise: that translation purpose, not machine-generated patterns, should guide normative adaptation, ensuring the target text serves its intended communicative function in the target context.

The conclusion of this study on revisiting Skopos Theory in the era of AI translation serves as both a synthesis of key findings and a directional compass for future translation practice and research, anchoring the theoretical explorations of translation purpose and machine mediation in the practical context of contemporary language services. At its core, Skopos Theory’s foundational tenet—that the purpose of translation dictates its strategies—has been reaffirmed as a resilient framework, yet one that requires nuanced adaptation to accommodate the transformative role of AI tools. Throughout the analysis, we have established that AI-mediated translation does not negate the teleological core of Skopos Theory; instead, it introduces a new layer of “machine agency” that interacts with the translator’s intentionality, reshaping how translation purposes are conceptualized, operationalized, and evaluated.

A critical insight from this study is the redefinition of the “translation purpose” itself in the AI era. Traditionally framed as a goal set by human stakeholders (such as clients, translators, or target audiences), the purpose now emerges as a collaborative construct co-shaped by human intent and machine capabilities. For instance, when a translator leverages a large language model (LLM) to translate a medical brochure, the purpose of “ensuring clinical accuracy and patient accessibility” is not merely executed by the human’s editing but is also influenced by the LLM’s training data on medical terminology and its tendency to prioritize fluency over domain-specific precision. This interaction demands that translators adopt a “teleological calibration” approach: first, articulating the core purpose with explicit criteria (e.g., “95% terminological consistency with WHO guidelines”), then selecting an AI tool aligned with these criteria, and finally iteratively adjusting the machine output to align with the intended purpose. This operational pathway—centered on purpose-driven tool selection and human-machine co-calibration—addresses the gap between theoretical teleology and AI-driven practice, ensuring that Skopos Theory remains relevant to the workflows of modern translators.

Another pivotal contribution lies in clarifying the evaluative framework for AI-mediated translations through the lens of Skopos Theory. Traditional evaluation metrics (e.g., BLEU scores) often prioritize formal equivalence, which fails to capture whether the translation fulfills its intended purpose. This study proposes a purpose-centric evaluation model that integrates both machine-generated performance indicators and human judgment of teleological achievement. For example, evaluating an AI-translated marketing campaign would involve not only measuring lexical accuracy but also assessing whether the output achieves the purpose of “resonating with the target audience’s cultural values” (e.g., a Chinese-to-English translation of a tea brand slogan that retains the metaphor of “harmony” while adapting it to Western consumer perceptions of “natural balance”). This model elevates the role of human evaluators as guardians of teleological integrity, ensuring that AI tools do not reduce translation to a mechanical task but serve as enablers of purposeful communication.

The practical importance of these findings extends beyond individual translators to the broader translation industry. Language service providers (LSPs) can apply the purpose-driven tool-selection framework to optimize their AI adoption strategies, reducing costs while maintaining quality consistency across projects. For academic researchers, this study opens avenues for empirical investigations into how different AI tools (e.g., neural machine translation [NMT] vs. rule-based systems) interact with specific translation purposes (e.g., literary creativity vs. technical documentation). Additionally, the ethical implications of teleological alignment in AI translation—such as ensuring that AI tools do not inadvertently subvert the purpose of “promoting cultural sensitivity” through biased training data—are highlighted as a critical area for future inquiry.

In essence, this study demonstrates that Skopos Theory, when adapted to the AI era, remains a cornerstone of translation theory, providing a teleological anchor that prevents AI from becoming a dehumanizing force in language mediation. The future of translation lies in a symbiotic relationship between human intentionality and machine efficiency, where the purpose of translation—whether to inform, persuade, or bridge cultures—guides every step of the human-machine collaboration. By revisiting Skopos Theory through the prism of AI, we have not only preserved its theoretical essence but also empowered translators to navigate the complexities of modern translation with clarity, purpose, and confidence.