AI and Translation: Augmentation, Not Replacement

This week’s entry was written by Stephanie Rodríguez. She is an Assistant Teaching Professor in the Spanish and Portuguese Department, SASN and Director of Lives in Translation, the Translation and Interpreting program at RU-N. Rodríguez is a current Scholar-in-Residence for 2024-2025 – learn more about her project here. 

Almost two years have passed since the launch of GPT, a large language model (LLM) that has revolutionized many aspects of education and professional practice. As an educator and translator, I am continuously learning about the latest trends in translation pedagogy and the language services industry to exploring ways in guiding students to ethically and responsibly engaging with AI while learning about translation and interpreting studies.   In this post, I share my experience on integrating AI-driven translation technologies in the classroom, discuss the concept of human-centered AI in translation, and provide key takeaways to help educators navigate this evolving landscape.  

A common question my students ask is, “Will AI replace human translators?”. Although I recognize the concerns driving this question, data-driven research suggests that while AI is making notable strides in translation technology, there is far more to translation than a simple one-to-one equivalence between languages. Culture, nuance, and context are deeply woven into every language, making human expertise essential to producing high-quality, context-aware translations.  

Since I began teaching translation studies in 2020, we have explored tools like computer-assisted translation (CAT), translation memories (TM), and neural machine translation (NMT). Now, with LLM-based tools (e.g., GPT or Google’s Gemini), the technological landscape has evolved yet again, but the fundamental principles remain the same:  

1. Accuracy and readability for the target reader  

2. Language development for effective communication  

3. Critical evaluation to ensure translation quality  

4. The influence of culture and society on language  

5. Understanding the evolving role of a translator   

6. Ethical and responsible use of technology  

What is Machine Translation?  

A helpful starting point in discussing AI and translation is defining machine translation (MT) and identifying the characteristics of a good quality translation. As we explore these concepts, I invite students to:  

• Learn the capabilities of NMT and LLMs in MT: Students acquire a foundational understanding of translation technology by demystifying MT and the role of NMT and LLMs. After gaining this knowledge, students assess the quality and apply the theory to practice by giving it a try themselves.   

• Examine raw machine translation outputs: By studying unedited AI-generated translations, students learn to examine texts for inaccuracies, missing context, awkward phrasings, repetition issues, or wrong terminology.   

• Engage in post-editing of MT: Students practice adjusting machine-translated content to improve fluency, accuracy, and cultural appropriateness.   

• Navigate ethical considerations: Through scenario-based exercises, students address ethical concerns that may arise when using AI tools such as, biases, confidentiality, mistranslations, overtranslations, or translations with incorrect terminology. Most importantly, while considering the perception of the end user of the tool for language solutions, in this case non-English speakers who are experiencing a linguistic barrier, and the risk settings.   

• Understand risk factors: The effectiveness of AI-driven translation is highly context-dependent. In high-stakes situations, such as filling out a legal form or patient medical history form, translation errors can have severe, life altering consequences. By contrast, inaccuracies in a low-risk context such as translating a recipe, usually results in no more than a minor inconvenience. Understanding the spectrum of risk is crucial for determining when, if, and how to use AI-driven translations.   

• Prompt engineering: Students are encouraged to familiarize themselves with the inner workings of AI in translation, to examine how prompts shape the system’s outputs, gaining insights into its capabilities and limitations. This allows students to identify obstacles and prepare for troubleshooting in real-world translation scenarios.   

Through this series of hands-on, real-world exercises, we discuss and acknowledge that AI in translation is best understood as a tool rather than a replacement for human expertise.  AI-human collaboration delivers culturally and contextually accurate translations.   

What is Human-Centered AI?  

A valuable framework I have started implementing in the classroom is human-centered artificial intelligence (HCAI). This approach shifts the perspective from technology as a replacement for human labor to one of human enhancement. In class we discuss the work from Ben Shneiderman that reframes the “human-in-the-loop” concept in AI to “IA”—Intelligence Amplification—where humans remain the central decision-makers, and AI tools operate around human needs.  

By applying this framework to the role of AI in translation, I discuss its function with my students as a supportive tool that amplifies human capabilities, while still allowing translators to be the experts on complex language solutions, cultural nuances, and creative re-expression in the translated text.  

Contrary to fears about AI-driven “replacement,” HCAI aligns with a “shift” in the translation process rather than an elimination of human involvement.   

Through this shift, students are motivated to:  

• Sharpen critical thinking skills: Engaging with AI outputs demands a deeper linguistic analysis, prompting students to become more reflective about common AI-generated errors.   

• Remain intellectually curious: As we address the role of a translator in an ever-changing linguistic setting, students are encouraged to remain in the know of the latest language updates, technology trends, and how this shifts their work as a translator.   

• Maintain a healthy skepticism: While welcoming advancements in the field, it remains essential to continue to examine the use of new technologies, carefully assessing the advantages and inherent challenges or limitations.   

By teaching future translators to be adept in both the linguistic and technological dimensions of their work, I aim to prepare students to navigate an evolving industry with confidence, ethical responsibility, and a specialized skillset tailored to their role.  

Key Takeaways for Educators  

AI Augments, Not Replaces: Although AI is changing the rules of the game, human intelligence remains the driver, as humans are able to shift thinking and develop analysis without being prompted.  

Students Remain at the Core: While AI is a newcomer to the college classroom, the spotlight firmly remains on the students engaging with one another through thought-provoking discussions.  

Remain in the Know: Keeping up with the latest AI developments, including its strengths and weaknesses, allows for a clearer understanding of its role and guides ethical, thoughtful integration. This will inspire students (and us, as faculty) to remain curious.   

Resources:   

Engineering a Future of Opportunity with Siya Raj Purohit, Education at OpenAI   

Upcoming Event – Lives in Translation:  

Lives in Translation and Institute of Data Research and Information Science (IDRIS)  

Joint Faculty Workshop 

Neither Magic Wand nor Enemy: The Responsible Use of AI in Teaching the Humanities and Social Sciences  Wednesday, April 16, 2025 2:30 PM - 3:50 PM Dana Library | Room 320 – Register Here  

Upcoming Events P3:  

Effective Mentoring to Improve Student Outcomes - March 12

Register Here

Spring 2025 Write-on-Site - March 12 

Register Here 

Spring 2025 Write-on-Site - April 9 

Register Here   

SMARTeaching: Fostering Student Belonging in Prison-Based College Courses - April 9

Register Here

Spring 2025 Write-on-Site - May 5

Register Here