AI scoring vs human scoring for language tests: What's the difference?

A girl sat at a desk with a laptop and notepad studying and taking notes
Reading time: 6 minutes

When entering the world of language proficiency tests, test takers are often faced with a dilemma: Should they opt for tests scored by humans or those assessed by artificial intelligence (AI)? The choice might seem trivial at first, but understanding the differences between AI scoring and human language test scoring can significantly impact preparation strategy and, ultimately, determine test outcomes.

The human touch in language proficiency testing and scoring

Historically, language tests have been scored by human assessors. This method leverages the nuanced understanding that humans have of language, including idiomatic expressions, cultural references, and the subtleties of tone and even writing style, akin to the capabilities of the human brain. Human scorers can appreciate the creative and original use of language, potentially rewarding test takers for flair and originality in their answers. Scorers are particularly effective at evaluating progress or achievement tests, which are designed to assess a student's language knowledge and progress after completing a particular chapter, unit, or at the end of a course, reflecting how well the language tester is performing in their language learning studies.

One significant difference between human and AI scoring is how they handle context. Human scorers can understand the significance and implications of a particular word or phrase in a given context, while AI algorithms rely on predetermined rules and datasets.

The adaptability and learning capabilities of human brains contribute significantly to the effectiveness of scoring in language tests, mirroring how these brains adjust and learn from new information.

Advantages:

  • Nuanced understanding: Human scorers are adept at interpreting the complexities and nuances of language that AI might miss.
  • Contextual flexibility: Humans can consider context beyond the written or spoken word, understanding cultural and situational implications.

Disadvantages:

  • Subjectivity and inconsistency: Despite rigorous training, human-based scoring can introduce a level of subjectivity and variability, potentially affecting the fairness and reliability of scores.
  • Time and resource intensive: Human-based scoring is labor-intensive and time-consuming, often resulting in longer waiting times for results.
  • Human bias: Assessors, despite being highly trained and experienced, bring their own perspectives, preferences and preconceptions into the grading process. This can lead to variability in scoring, where two equally competent test takers might receive different scores based on the scorer's subjective judgment.

The rise of AI in language test scoring

With advancements in technology, AI-based scoring systems have started to play a significant role in language assessment. These systems utilize algorithms and natural language processing (NLP) techniques to evaluate test responses. AI scoring promises objectivity and efficiency, offering a standardized way to assess language and proficiency level.

Advantages:

  • Consistency: AI scoring systems provide a consistent scoring method, applying the same criteria across all test takers, thereby reducing the potential for bias.
  • Speed: AI can process and score tests much faster than human scorers can, leading to quicker results turnaround.
  • Great for more nervous testers: Not everyone likes having to take a test in front of a person, so AI removes that extra stress.

Disadvantages:

  • Lack of nuance recognition: AI may not fully understand subtle nuances, creativity, or complex structures in language the way a human scorer can.
  • Dependence on data: The effectiveness of AI scoring is heavily reliant on the data it has been trained on, which can limit its ability to interpret less common responses accurately.

Making the choice

When deciding between tests scored by humans or AI, consider the following factors:

  • Your strengths: If you have a creative flair and excel at expressing original thoughts, human-scored tests might appreciate your unique approach more. Conversely, if you excel in structured language use and clear, concise expression, AI-scored tests could work to your advantage.
  • Your goals: Consider why you're taking the test. Some organizations might prefer one scoring method over the other, so it's worth investigating their preferences.
  • Preparation time: If you're on a tight schedule, the quicker turnaround time of AI-scored tests might be beneficial.

Ultimately, both scoring methods aim to measure and assess language proficiency accurately. The key is understanding how each approach aligns with your personal strengths and goals.

The bias factor in language testing

An often-discussed concern in both AI and human language test scoring is the issue of bias. With AI scoring, biases can be ingrained in the algorithms due to the data they are trained on, but if the system is well designed, bias can be removed and provide fairer scoring.

Conversely speaking, human scorers, despite their best efforts to remain objective, bring their own subconscious biases to the evaluation process. These biases might be related to a test taker's accent, dialect, or even the content of their responses, which could subtly influence the scorer's perceptions and judgments. Efforts are continually made to mitigate these biases in both approaches to ensure a fair and equitable assessment for all test takers.

Preparing for success in foreign language proficiency tests

Regardless of the scoring method, thorough preparation remains, of course, crucial. Familiarize yourself with the test format, practice under timed conditions, and seek feedback on your performance, whether from teachers, peers, or through self-assessment tools.

The distinctions between AI scoring and human in language tests continue to blur, with many exams now incorporating a mix of both to have students leverage their respective strengths. Understanding and interpreting written language is essential in preparing for language proficiency tests, especially for reading tests. By understanding these differences, test takers can better prepare for their exams, setting themselves up for the best possible outcome.

Will AI replace human-marked tests?

The question of whether AI will replace markers in language tests is complex and multifaceted. On one hand, the efficiency, consistency and scalability of AI scoring systems present a compelling case for their increased utilization. These systems can process vast numbers of tests in a fraction of the time it takes markers, providing quick feedback that is invaluable in educational settings. On the other hand, the nuanced understanding, contextual knowledge, flexibility, and ability to appreciate the subtleties of language that human markers bring to the table are qualities that AI has yet to fully replicate.

Both AI and human-based scoring aim to accurately assess language proficiency levels, such as those defined by the Common European Framework of Reference for Languages or the Global Scale of English, where a level like C2 or 85-90 indicates that a student can understand virtually everything, master the foreign language perfectly, and potentially have superior knowledge compared to a native speaker.

The integration of AI in language testing is less about replacement and more about complementing and enhancing the existing processes. AI can handle the objective, clear-cut aspects of language testing, freeing markers to focus on the more subjective, nuanced responses that require a human touch. This hybrid approach could lead to a more robust, efficient and fair assessment system, leveraging the strengths of both humans and AI.

Future developments in AI technology and machine learning may narrow the gap between AI and human grading capabilities. However, the ethical considerations, such as ensuring fairness and addressing bias, along with the desire to maintain a human element in education, suggest that a balanced approach will persist. In conclusion, while AI will increasingly play a significant role in language testing, it is unlikely to completely replace markers. Instead, the future lies in finding the optimal synergy between technological advancements and human judgment to enhance the fairness, accuracy and efficiency of language proficiency assessments.

Tests to let your language skills shine through

Explore ɫèAV's innovative language testing solutions today and discover how we are blending the best of AI technology and our own expertise to offer you reliable, fair and efficient language proficiency assessments. We are committed to offering reliable and credible proficiency tests, ensuring that our certifications are recognized for job applications, university admissions, citizenship applications, and by employers worldwide. Whether you're gearing up for academic, professional, or personal success, our tests are designed to meet your diverse needs and help unlock your full potential.

Take the next step in your language learning journey with ɫèAV and experience the difference that a meticulously crafted test can make.

Explore our wide range of English language tests

More blogs from ɫèAV

  • Two teenagers sat at a desk in a classroom working together in front of a laptop

    My lifelong learning journey: Why learning English never stops

    By Zarela Cruz
    Reading time: 4 minutes

    My journey with English began in the unlikeliest of places: a mining camp in southern Peru. As a child, I was fascinated by American culture – the movies, the music, the seemingly limitless world that English opened up. For me, the language was a gateway leading to a deeper understanding and feeling of belonging, making me part of their culture.

  • A classroom scene with a teacher and diverse students engaged in learning, using laptops on desks, in a brightly lit room.

    Is game-based learning technology a waste of time?

    By
    Reading time: 4 minutes

    We feel that game-based learning (GBL) is a waste of time… if not properly understood. Even then, one could argue that "wasting time" is a vital part of learning and perhaps we need to stop insisting that every second counts.

    Game-based learning vs. Gamification: Understanding the difference

    Let’s begin by first addressing the term "game-based learning" and how it compares to its doppelgänger, gamification. Gamification is the application of game mechanics and dynamics to non-game contexts to solve problems, engage users and promote desired behaviours. For example, rewarding acheivements with points, awards or badges for achievement, levelling up, using avatars, quests and collaboration are all gamification features, which elevate games above the mundane activities of normal life.

    The principles of gamification have been applied to the retail and services sectors for years: think of airlines' frequent flyers programs, pubs and bars running "happy hours" promotions, WeightWatchers' points-counting, Foursquare's badges for visiting new places. There are hundreds of examples of where game dynamics have been introduced into non-game contexts to influence behaviour and bring about a desired result.

    Game-based learning in action: More than just play

    Gamification does not refer to the straightforward use of games (whether digital or otherwise) as part of a teaching or learning interaction. A teacher using the board game Monopoly in the classroom to demonstrate the idea of rent is not gamifying the learning environment; they are involving learners in game-based learning. GBL refers to the use of games as tools: as devices for opening discussion, presenting concepts or promoting learner engagement within clearly defined learning objectives, in other words, learning through playing games. Gamification is the appropriation of those principles, mechanics and dynamics that make games work in order to promote engagement or engender a desired outcome.

    Teachers have long introduced games into the learning environment – such as Kim’s Game, Pelmanism and Guess Who? – and the uptake of GBL with digital games is particularly well demonstrated by the work being done by the Institute of Play, and the growing popularity of the likes of Minecraft and SimCity in schools. In terms of our immediate ELT context, pretty much any video game can be repurposed for language learning, in the same way a text, song or website can be. Take, for example, the indie game . Although there is no actual spoken language in the game, the platform puzzle format lends itself perfectly to practising language around predictions or conditionals ("If I pull that lever, the door will open"), recounting events ("I was chased by a giant spider!"), strategising and so on. In this post, we will be referring to existing digital games that have been appropriated into a learning context, as opposed to games that have been designed with a specific educational use in mind.

    Sharma and Barrett’s definition of blended learning provides a useful context for approaching the use of digital games in such modalities, notably the combination of “a face-to-face classroom component with an appropriate use of technology”. We’re making no assumptions about whether the games are being accessed in the classroom, on mobile or online at home. The blend isn’t defined by where a learner is, but by how their use of technology supports and enhances their contact with the teacher.

    The notion of appropriateness in Sharma and Barrett’s definition is critical as, in the case of a GBL project, it assumes a teacher has a familiarity with both the tech and content accessibility of a selected game. Acquiring that level of familiarity with a game requires a certain amount of time engaging with it to determine its fit for the needs of the learners, an activity which might easily be considered off task when compared to the other demands being made on an educator’s schedule.

    How GBL fuels engagement and deeper learning

    The benefits of GBL with digital games are potentially quite profound, however. First, studies indicate that playing video games in general can stimulate the generation of neurons and enhance connectivity between the regions of the brain responsible for memory formation, spatial orientation and strategic thinking. The right pairing of game and learning objectives could be argued to promote situated cognition, a theory that knowledge is constructed through – and inseparable from – social interactions and the context in which they take place. A learner immersed in SimCity stands a much greater chance of understanding the principles of taxation and the provision of public services through playing the role of a mayor, for example, than a learner being walked through the annual budget. As Lim et al. state: “games are effective because learning takes place within a meaningful context where what must be learned is directly related to the environment in which learning and demonstration take place”.

    Early-stage research on mirror neurons is adding a new aspect to the discussion around the immediacy of playing games. In short, mirror neurons suggest that when we observe someone performing an action, there is a brief moment in which our brain cells fire as if we are carrying out that action ourselves. The boundary between observer (player) and observed (in-game character) becomes blurred for a split second. A lot can happen in that split second.

    In addition to the benefits of an immersive, neuron-stroking experience, games demonstrably promote learner engagement by introducing the F-bomb into the mix (fun). They also have the capacity to provide an unrivalled social experience, as in the case of MMORPGs (massively multiplayer online role-playing games). As an example, consider how a leading MMORPG game, World of Warcraft, is applied in learning environments.

    Addressing concerns and embracing change

    However, there are often deep-seated cultural aversions to the use of games in an educational context that GBL initiatives are required to overcome. Critics have said that digital games are anti-social, that they rot your attention span, that they are not legitimate, validated learning resources. Although there is not yet a body of research that can empirically confirm or debunk the effectiveness of games used for learning, surely watching a learner plan, execute and evaluate a project in Minecraft with classmates suggests that those objections are based on dated assumptions. The language learning space in particular is still very much attached to a coursebook paradigm that is predicated on levels and a clearly defined syllabus. Perhaps GBL is too much at odds with an established business model that is the bedrock of too many large education organisations.

    So is GBL a waste of time? We’d argue that it is when its potential is not properly recognised and it is treated as light relief. Games are dynamic, engaging resources capable of delivering experiences and drawing connections that can really ignite a student’s learning experience. Furthermore, they bring a playful and unpredictable aspect to the learning process.

  • Four young adults are sitting together outdoors, engaged in conversation with books and a smartphone, near a modern building.

    10 top study tips for neurodivergent learners

    By
    Reading time: 5 minutes

    Every student deserves a chance to succeed. Neurodivergent students – those with ADHD, dyslexia, autism or other neurological differences – have special strengths and perspectives. But usual studying methods don’t always fit their needs. The good news is that with the right tools and strategies, neurodivergent learners can study better, feel less frustrated and reach their full potential.

    Here are a few tips to help neurodivergent learns optimize study sessions: