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

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    The ethical challenges of AI in education

    By Billie Jago
    Reading time: 5 minutes

    AI is revolutionising every industry, and language learning is no exception. AI tools can provide students with unprecedented access to things like real-time feedback, instant translation and AI-generated texts, to name but a few.

    AI can be highly beneficial to language education by enhancing our students’ process of learning, rather than simply being used by students to ‘demonstrate’ a product of learning. However, this is easier said than done, and given that AI is an innovative tool in the classroom, it is crucial that educators help students to maintain authenticity in their work and prevent AI-assisted ‘cheating’. With this in mind, striking a balance between AI integration and academic integrity is critical.

    How AI impacts language learning

    Generative AI tools such as ChatGPT and Gemini have made it easier than ever for students to refine and develop their writing. However, these tools also raise concerns about whether submitted texts are student-produced, and if so, to what extent. If students rely on text generation tools instead of their own skills, our understanding of our students’ abilities may not reflect their true proficiency.

    Another issue is that if students continue to use AI for a skill they are capable of doing on their own, they’re likely to eventually lose that skill or become significantly worse at it.

    These points create a significant ethical dilemma:

    • How does AI support learning, or does it (have the potential to) replace the learning process?
    • How can educators differentiate between genuine student ability and AI-assisted responses?

    AI-integration strategies

    There are many ways in which educators can integrate AI responsibly, while encouraging our learners to do so too.

    1.ÌýRedesign tasks to make them more ‘AI-resistant’

    No task can be completely ‘AI-resistant’, but there are ways in which teachers can adapt coursebook tasks or take inspiration from activities in order to make them less susceptible to being completed using AI.

    For example:

    • Adapt writing tasks to be hyperlocal or context-specific. Generative AI is less likely to be able to generate texts that are context-bound. Focus on local issues and developments, as well as school or classroom-related topics. A great example is having students write a report on current facilities in their classroom and suggestions for improving the learning environment.
    • Focus on the process of writing rather than the final product. Have students use mind maps to make plans for their writing, have them highlight notes from this that they use in their text and then reflect on the steps they took once they’ve written their piece.
    • Use multimodal learning. Begin a writing task with a class survey, debate or discussion, then have students write up their findings into a report, essay, article or other task type.
    • Design tasks with skill-building at the core. Have students use their critical thinking skills to analyse what AI produces, creatively adapt its output and problem solve by fact-checking AI-generated text.

    2.ÌýUse AI so that students understand you know how to use it

    Depending on the policies in your institution, if you can use AI in the classroom with your students, they will see that you know about different AI tools and their output. A useful idea is to generate a text as a class, and have students critically analyse the AI-generated text. What do they think was done well? What could be improved? What would they have done differently?

    You can also discuss the ethical implications of AI in education (and other industries) with your students, to understand their view on it and better see in what situations they might see AI as a help or a hindrance.

    3.ÌýUse the GSE Learning Objectives to build confidence in language abilities

    Sometimes, students might turn to AI if they don’t know where to start with a task or lack confidence in their language abilities. With this in mind, it’s important to help your students understand where their language abilities are and what they’re working towards, with tangible evidence of learning. This is where the GSE Learning Objectives can help.

    The Global Scale of English (GSE) provides detailed, skill-specific objectives at every proficiency level, from 10 to 90. These can be used to break down complex skills into achievable steps, allowing students to see exactly what they need to do to improve their language abilities at a granular level.

    • Start by sharing the GSE Learning Objectives with students at the start of class to ensure they know what the expectations and language goals are for the lesson. At the end of the lesson, you can then have students reflect on their learning and find evidence of their achievement through their in-class work and what they’ve produced or demonstrated.
    • Set short-term GSE Learning Objectives for the four key skills – speaking, listening, reading and writing. That way, students will know what they’re working towards and have a clear idea of their language progression.
  • Students sat ina library studying with laptops in front of them chatting to eachother

    Teaching engaging exam classes for teenagers

    By Billie Jago
    Reading time: 4 minutes

    Teachers all over the world know just how challenging it can be to catch their students’ interest and keep them engaged - and it’s true whether you’re teaching online or in a real-world classroom.

    Students have different learning motivations; some may be working towards their exam because they want to, and some because they have to, and the repetitiveness of going over exam tasks can often lead to boredom and a lack of interest in the lesson.Ìý

    So, what can we do to increase students’ motivation and add variation to our classes to maintain interest?Ìý

    Engage students by adding differentiation to task types

    We first need to consider the four main skills and consider how to differentiate how we deliver exam tasks and how we have students complete them.Ìý

    Speaking - A communicative, freer practice activity to encourage peer feedback.

    Put students into pairs and assign them as A and B. Set up the classroom so pairs of chairs are facing each other - if you’re teaching online, put students in individual breakaway rooms.Ìý

    Hand out (or digitally distribute) the first part of a speaking exam, which is often about ‘getting to know you’. Have student A’s act as the examiner and B’s as the candidate.Ìý

    Set a visible timer according to the exam timings and have students work their way through the questions, simulating a real-life exam. Have ‘the examiners’ think of something their partner does well and something they think they could improve. You can even distribute the marking scheme and allow them to use this as a basis for their peer feedback. Once time is up, ask student B’s to move to the next ‘examiner’ for the next part of the speaking test. Continue this way, then ask students to switch roles.Ìý

    Note: If you teach online and your teaching platforms allow it, you can record the conversations and have students review their own performances. However, for privacy reasons, do not save these videos.

    Listening – A student-centered, online activity to practice listening for detail or summarising.

    Ask pairs of students to set up individual online conference call accounts on a platform like Teams or Zoom.Ìý

    Have pairs call each other without the video on and tell each other a story or a description of something that has happened for their partner to listen to. This could be a show they’ve watched, an album they’ve listened to, or a holiday they’ve been on, for example. Ask students to write a summary of what their partner has said, or get them to write specific information (numbers, or correctly spelt words) such as character or song names or stats, for example. Begin the next class by sharing what students heard. Students can also record the conversations without video for further review and reflection afterwards.

    Writing –ÌýA story-writing group activity to encourage peer learning.

    Give each student a piece of paper and have them draw a face at the top of the page. Ask them to give a name to the face, then write five adjectives about their appearance and five about their personality. You could also have them write five adjectives to describe where the story is set (place).Ìý

    Give the story’s opening sentence to the class, e.g. It was a cold, dark night and… then ask students to write their character’s name + was, and then have them finish the sentence. Pass the stories around the class so that each student can add a sentence each time, using the vocabulary at the top of the page to help them.Ìý

    Reading –ÌýA timed, keyword-based activity to help students with gist.

    Distribute a copy of a text to students. Ask them to scan the text to find specific words that you give them, related to the topic. For example, if the text is about the world of work, ask students to find as many jobs or workplace words as they can in the set amount of time. Have students raise their hands or stand up when they have their answers, award points, and have a whole class discussion on where the words are and how they relate to the comprehension questions or the understanding of the text as a whole.Ìý

    All 4 skills –ÌýA dynamic activity to get students moving.

    Set up a circuit-style activity with different ‘stations’ around the classroom, for example:Ìý

    • ListeningÌý
    • ReadingÌý
    • Writing (1 paragraph)Ìý
    • Use of English (or grammar/vocabulary).Ìý

    Set a timer for students to attempt one part from this exam paper, then have them move round to the next station. This activity can be used to introduce students to certain exam tasks, or a way to challenge students once they’ve built their confidence in certain areas.Ìý

  • A teachet stood in front of a class in front of a board, smiling at his students.

    How to assess your learners using the GSE Assessment Frameworks

    By Billie Jago
    Reading time: 4 minutes

    With language learning, assessing both the quality and the quantity of language use is crucial for accurate proficiency evaluation. While evaluating quantity (for example the number of words written or the duration of spoken production) can provide insights into a learner's fluency and engagement in a task, it doesn’t show a full picture of a learner’s language competence. For this, they would also need to be evaluated on the quality of what they produce (such as the appropriateness, accuracy and complexity of language use). The quality also considers factors such as grammatical accuracy, lexical choice, coherence and the ability to convey meaning effectively.

    In order to measure the quality of different language skills, you can use the Global Scale of English (GSE) assessment frameworks.

    Developed in collaboration with assessment experts, the GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help you assess the proficiency of your learners.

    There are two GSE Assessment Frameworks: one for adults and one for young learners.

    What are the GSE Assessment Frameworks?

    • The GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help teachers assess their learners’ proficiency of all four skills (speaking, listening, reading and writing).
    • The GSE Learning Objectives focus on the things a learner can do, while the GSE Assessment Frameworks focus on how well a learner can do these things.
    • It can help provide you with examples of what proficiencies your learners should be demonstrating.ÌýÌý
    • It can help teachers pinpoint students' specific areas of strength and weakness more accurately, facilitating targeted instruction and personalized learning plans.
    • It can also help to motivate your learners, as their progress is evidenced and they can see a clear path for improvement.

    An example of the GSE Assessment Frameworks

    This example is from the Adult Assessment Framework for speaking.

    As you can see, there are sub-skills within speaking (andÌýfor the other three main overarching skills – writing, listening and reading). Within speaking, these areÌýproductionÌýandÌýfluency, spoken interaction, language range andÌýaccuracy.

    The GSE range (and corresponding CEFR level) is shown at the top of each column, and there are descriptors that students should ideally demonstrate at that level.

    However, it is important to note that students may sit across different ranges, depending on the sub-skill. For example, your student may show evidence of GSE 43-50 production and fluency and spoken interaction, but they may need to improve their language range and accuracy, and therefore sit in a range of GSE 36-42 for these sub-skills.