Artificial Intelligence Archives

Big Data: The Future of Assessment

Leonardo Revilla, MScFebruary 14, 2025

Assessments have long been a cornerstone of education, hiring, and psychological research. However, with advancements in artificial intelligence and machine learning, these assessments are evolving faster than ever. Moreover, Big Data is revolutionizing how we measure personality, cognitive ability, and behavioral traits. From adaptive testing to bias detection, it is improving accuracy, efficiency, and fairness in psychological measurement. But with these advancements come challenges—concerns about privacy, bias, and ethical AI implementation.

In this article, we’ll break down how Big Data is transforming in testing, where it’s being applied, and what the future holds.

What is Big Data?

Big Data refers to large-scale, complex datasets that can be analyzed to reveal patterns, trends, and associations. In psychometrics, this means massive amounts of test-taker data—not just scores, but response times, keystroke dynamics, facial expressions, and even biometric data.

Traditional assessments relied on small samples and fixed test items. Today, platforms collect millions of data points in real time, enabling AI-driven insights that were previously impossible. Machine learning algorithms can analyze these rich datasets to detect patterns in behavior, predict future performance, and enhance the precision of assessments.

This shift has led to more precise, adaptive, and fairer assessments—but also raises ethical and practical concerns, such as data privacy, bias in algorithms, and the need for transparency in decision-making.

How Big Data is Transforming Assessment

1. Smarter Assessment

Big Data is used to drive the development, administration, and scoring of future-focused assessments. Some examples of this are process data, machine learning personalization, and adaptive testing. By leveraging data-driven decision making inside the assessment, we can make it smarter, faster, and fairer.

Process data refers to the use of data other than answer selection, such as keystroke/mouse dynamics. An example is a drag-and-drop question where a student has to classify animals into Reptile, Mammal, or Amphibian; instead of just recording the final locations, we can evaluate what they dragged first and where, if they changed their mind, how much time they took to answer the question, etc. This can provide greater insight into both scoring and feedback. We can also evaluate the use of tools like rulers or calculators (Liao & Sahin, 2020). However, it might be of limited use in high-stakes exams where the final answer is what matters.

Machine learning algorithms based on Big Data can improve assessment by personalizing the assessment or even the selection of assessment. Next-generation learning systems are designed to be adaptive from a very high level, understanding what students know and recommending the next modules, not unlike how your favorite video streaming service learns what shows you like or not and recommends new ones. This could also be done inside the assessment, transforming it from assessment of learning (AoL) to assessment for learning (AfL).

Big Data can enhance Computerized Adaptive Testing (CAT), where test difficulty adjusts in real time based on the test-taker’s responses. Instead of presenting a fixed set of questions, the algorithm selects each new question based on the test-taker’s previous answers, making the assessment more efficient and tailored to individual ability levels. This approach has been utilized for decades with large-scale exams, with well-known benefits like shorter tests, reduced anxiety, and increased engagement. The Graduate Management Admission Test (GMAT) transitioned to a computer-adaptive format in 1997. Similarly, the Graduate Record Examination (GRE) introduced a computer-adaptive format in 1993 and made it mandatory in 1997. You can read more about adaptive testing in action here.

2. AI-Driven Personality and Behavioral Analysis

Psychometric models like the Big Five and HEXACO are now enhanced by machine learning and natural language processing (NLP).

AI can analyze how people respond, not just what they answer—including text responses, speech patterns, and decision-making behaviors. Companies like HireVue analyze facial expressions and speech cadence to assess job candidates’ traits.

3. Detecting Bias and Improving Fairness

One of the biggest concerns in psychological testing is bias. Traditional tests have been criticized for favoring certain demographics over others.

With Big Data, AI can flag biased questions by analyzing how different groups respond.
Example: If women or minority groups consistently underperform on a test question despite having equal qualifications, the item may be unfair and flagged for review.

This helps ensure that assessments are more equitable and inclusive.

4. Predicting Job Performance and Talent Retention

Companies are increasingly using psychometric Big Data to predict:

Which candidates will succeed in a role
Who is at risk of burnout
How leadership potential can be identified early

Example: Google uses Big Data psychometric analysis to refine its hiring process, ensuring long-term employee success and cultural fit. You can read a more in-depth look at their hiring process here.

5. Real-Time Fraud Detection in Assessments

Just like SIFT detects test fraud, Big Data helps identify cheating in online exams. AI can analyze eye movement, response times, and typing behavior to detect suspicious activity. The models to do so are trained on large data sets; for example, a proctoring provider might have millions of past exam data, with stored videos as well as human-confirmed flags of if they were cheating or not. Universities and companies now use AI-powered proctoring tools to prevent cheating in high-stakes tests.

The Challenges of Big Data in Assessment

The integration of Big Data into psychometrics offers significant advancements but also presents several challenges that must be carefully managed:

1. Privacy and Data Security Risks

Collecting extensive psychological data introduces serious ethical concerns. Regulations like the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States mandate strict data protection protocols to safeguard individuals’ personal information. Ensuring compliance with these laws is essential to protect test-takers’ confidentiality and maintain public trust.

2. Algorithmic Bias in AI

AI models trained on historical data can inadvertently perpetuate existing biases if not properly managed. A notable example is Amazon’s hiring algorithm, which was discontinued after it demonstrated bias against female candidates. This incident underscores the importance of developing strategies to identify and mitigate bias in AI-driven assessments to ensure fairness and equity. You can read more about the incident here.

3. Necessity of Human Oversight

While AI-driven assessments can provide valuable insights, human oversight remains crucial. Misinterpretation or overreliance on automated results can lead to incorrect hiring decisions, misdiagnoses, or unethical outcomes. Professionals in psychology and human resources play a vital role in interpreting data within the appropriate context, ensuring that decisions are informed by both technological tools and human judgment.

Addressing these challenges requires a balanced approach that leverages the benefits of Big Data and AI while implementing safeguards to protect individual rights and uphold ethical standards.

The Future of Big Data in Psychometrics

The integration of Big Data into psychometrics is paving the way for innovative advancements that promise to enhance the precision, security, and ethical standards of assessments. Here are some emerging trends:

1. Wearable Biometric Assessments

The incorporation of data from wearable devices, such as heart rate monitors and electroencephalogram (EEG) sensors, into cognitive tests is on the rise. These devices provide real-time physiological data that can offer deeper insights into a test-taker’s cognitive and emotional states, potentially leading to more comprehensive assessments. For instance, in healthcare, wearable technology has been utilized for continuous monitoring and personalized care, highlighting its potential application in psychometric evaluations.

2. More Transparent AI Models

The demand for explainable AI in assessments is growing. Developing AI models that provide clear, understandable explanations for their decisions is crucial to ensure ethical use and to mitigate biases. This transparency is essential for building trust in AI-driven assessments and for adhering to ethical standards in testing. You can learn about that here.

3. Blockchain for Secure Testing

Blockchain technology is being explored for enhancing the security and integrity of assessments. Its decentralized ledger system can be used for secure credential verification, ensuring that test results are tamper-proof and verifiable. This approach can uphold the integrity of assessments and protect against fraudulent activities.

4. Integration of Assessment with Learning

The future of testing includes a closer integration of assessment and learning processes. Adaptive learning platforms that utilize Big Data can provide personalized learning experiences, adjusting content and assessments in real-time to meet individual learner needs. This approach not only enhances learning outcomes but also provides continuous assessment data that can inform educational strategies.

5. Greater Insight into Open-Response Items

Advancements in natural language processing (NLP) and machine learning are enabling more sophisticated analysis of open-response items in assessments. These technologies can evaluate the content, structure, and sentiment of written responses, providing deeper insights into test-takers’ understanding, reasoning, and communication skills. This development allows for a more nuanced evaluation of competencies that are difficult to measure with traditional multiple-choice questions.

As Big Data continues to shape the field of psychometrics, it is imperative to balance innovation with ethical responsibility. Ensuring data privacy, mitigating biases, and maintaining transparency are crucial considerations as we advance toward more sophisticated and personalized assessment methodologies.

Final Thoughts: The Future is Here, but Caution is Key

Big Data is revolutionizing assessment, making tests faster, more precise, and highly adaptive. AI-driven testing can unlock deeper insights into human cognition and behavior, transforming everything from education to hiring. However, these advancements come with profound ethical dilemmas—issues of privacy, bias, and opaque AI decision-making must be addressed to ensure responsible implementation.

To move forward, assessment professionals must embrace AI’s potential while demanding fairness, transparency, and rigorous data governance. The future of testing is not just about innovation but about striking the right balance between technology and ethical responsibility. As we harness AI to enhance assessments, we must remain vigilant in protecting individuals’ rights and ensuring that data-driven insights serve to empower rather than exclude.

February 14, 2025/by Leonardo Revilla, MSc

What is Automated Essay Scoring?

Nathan Thompson, PhDApril 25, 2023

Automated essay scoring (AES) is an important application of machine learning and artificial intelligence to the field of psychometrics and assessment. In fact, it’s been around far longer than “machine learning” and “artificial intelligence” have been buzzwords in the general public! The field of psychometrics has been doing such groundbreaking work for decades.

So how does AES work, and how can you apply it?

Request info about SmartMarq, our platform for AI and human essay scoring

What is automated essay scoring?

The first and most critical thing to know is that there is not an algorithm that “reads” the student essays. Instead, you need to train an algorithm. That is, if you are a teacher and don’t want to grade your essays, you can’t just throw them in an essay scoring system. You have to actually grade the essays (or at least a large sample of them) and then use that data to fit a machine learning algorithm. Data scientists use the term train the model, which sounds complicated, but if you have ever done simple linear regression, you have experience with training models.

There are three steps for automated essay scoring:

Establish your data set. Begin by gathering a substantial collection of student essays, ensuring a diverse range of topics and writing styles. Each essay should be meticulously graded by human experts to create a reliable and accurate benchmark. This data set forms the foundation of your automated scoring system, providing the necessary examples for the machine learning model to learn from.
Determine the features. Identify the key features that will serve as predictor variables in your model. These features might include grammar, syntax, vocabulary usage, coherence, structure, and argument strength. Carefully selecting these attributes is crucial as they directly impact the model’s ability to assess essays accurately. The goal is to choose features that are indicative of overall writing quality and are relevant to the scoring criteria.
Train the machine learning model. Use the established data set and selected features to train your machine learning model. This involves feeding the graded essays into the model, allowing it to learn the relationship between the features and the assigned grades. Through iterative training and validation processes, the model adjusts its algorithms to improve accuracy. Continuous refinement and testing ensure that the model can reliably score new, unseen essays with a high degree of precision.

Here’s an extremely oversimplified example:

You have a set of 100 student essays, which you have scored on a scale of 0 to 5 points.
The essay is on Napoleon Bonaparte, and you want students to know certain facts, so you want to give them “credit” in the model if they use words like: Corsica, Consul, Josephine, Emperor, Waterloo, Austerlitz, St. Helena. You might also add other Features such as Word Count, number of grammar errors, number of spelling errors, etc.
You create a map of which students used each of these words, as 0/1 indicator variables. You can then fit a multiple regression with 7 predictor variables (did they use each of the 7 words) and the 5 point scale as your criterion variable. You can then use this model to predict each student’s score from just their essay text.

Obviously, this example is too simple to be of use, but the same general idea is done with massive, complex studies. The establishment of the core features (predictive variables) can be much more complex, and models are going to be much more complex than multiple regression (neural networks, random forests, support vector machines).

Here’s an example of the very start of a data matrix for features, from an actual student essay. Imagine that you also have data on the final scores, 0 to 5 points. You can see how this is then a regression situation.

Examinee	Word Count	i_have	best_jump	move	and_that	the_kids	well
1	307	0	1	2	0	0	1
2	164	0	0	1	0	0	0
3	348	1	0	1	0	0	0
4	371	0	1	1	0	0	0
5	446	0	0	0	0	0	2
6	364	1	0	0	0	1	1

How do you score the essay?

If they are on paper, then automated essay scoring won’t work unless you have an extremely good software for character recognition that converts it to a digital database of text. Most likely, you have delivered the exam as an online assessment and already have the database. If so, your platform should include functionality to manage the scoring process, including multiple custom rubrics. An example of our FastTest platform is provided below.

Some rubrics you might use:

Grammar
Spelling
Content
Style
Supporting arguments
Organization
Vocabulary / word choice

How do you pick the Features?

This is one of the key research problems. In some cases, it might be something similar to the Napoleon example. Suppose you had a complex item on Accounting, where examinees review reports and spreadsheets and need to summarize a few key points. You might pull out a few key terms as features (mortgage amortization) or numbers (2.375%) and consider them to be Features. I saw a presentation at Innovations In Testing 2022 that did exactly this. Think of them as where you are giving the students “points” for using those keywords, though because you are using complex machine learning models, it is not simply giving them a single unit point. It’s contributing towards a regression-like model with a positive slope.

In other cases, you might not know. Maybe it is an item on an English test being delivered to English language learners, and you ask them to write about what country they want to visit someday. You have no idea what they will write about. But what you can do is tell the algorithm to find the words or terms that are used most often, and try to predict the scores with that. Maybe words like “jetlag” or “edification” show up in students that tend to get high scores, while words like “clubbing” or “someday” tend to be used by students with lower scores. The AI might also pick up on spelling errors. I worked as an essay scorer in grad school, and I can’t tell you how many times I saw kids use “ludacris” (name of an American rap artist) instead of “ludicrous” when trying to describe an argument. They had literally never seen the word used or spelled correctly. Maybe the AI model finds to give that a negative weight. That’s the next section!

How do you train a model?

Well, if you are familiar with data science, you know there are TONS of models, and many of them have a bunch of parameterization options. This is where more research is required. What model works the best on your particular essay, and doesn’t take 5 days to run on your data set? That’s for you to figure out. There is a trade-off between simplicity and accuracy. Complex models might be accurate but take days to run. A simpler model might take 2 hours but with a 5% drop in accuracy. It’s up to you to evaluate.

If you have experience with Python and R, you know that there are many packages which provide this analysis out of the box – it is a matter of selecting a model that works.

How effective is automated essay scoring?

Well, as psychometricians love to say, “it depends.” You need to do the model fitting research for each prompt and rubric. It will work better for some than others. The general consensus in research is that AES algorithms work as well as a second human, and therefore serve very well in that role. But you shouldn’t use them as the only score; of course, that’s impossible in many cases.

Here’s a graph from some research we did on our algorithm, showing the correlation of human to AES. The three lines are for the proportion of sample used in the training set; we saw decent results from only 10% in this case! Some of the models correlated above 0.80 with humans, even though this is a small data set. We found that the Cubist model took a fraction of the time needed by complex models like Neural Net or Random Forest; in this case it might be sufficiently powerful.

How can I implement automated essay scoring without writing code from scratch?

There are several products on the market. Some are standalone, some are integrated with a human-based essay scoring platform. ASC’s platform for automated essay scoring is SmartMarq; click here to learn more. It is currently in a standalone approach like you see below, making it extremely easy to use. It is also in the process of being integrated into our online assessment platform, alongside human scoring, to provide an efficient and easy way of obtaining a second or third rater for QA purposes.

Want to learn more? Contact us to request a demonstration.

April 25, 2023/by Nathan Thompson, PhD

What ChatGPT brings us for item writing

Laila Issayeva M.Sc.April 9, 2023

Even those ones who do not consider themselves technology buffs have heard about ChatGPT. Today, everybody is talking about ChatGPT—a modern megastar of artificial intelligence (AI) and machine learning (ML) industries. This is how ChatGPT introduces itself:

“I am an AI language model called ChatGPT. My purpose is to assist and communicate with users through text-based conversations, and to provide helpful and informative responses to their questions and inquiries.”

I realized its sense straight away but still decided to interact with ChatGPT more closely while working on item writing for a Math test. I opened this link and initiated a new conversation—stupid simple. Below I am sharing my observations of ChatGPT features, classifying them as its strengths and imperfections. I will then discuss my experience in item writing with ChatGPT.

Strengths of ChatGPT

ChatGPT is an intelligent chatbot that

is polite,
declines inadequate requests,
generates responses very quickly,
allows user to provide follow-up adjustments,
remembers what user said earlier in the conversation,
can significantly reduce time for item writing to subject matter experts.

Imperfections of ChatGPT

General ones

Free version of ChatGPT has a restricted number of symbols produced (around 2,000). Therefore, if you need to construct a test, like me, you need to task the chatbot in portions or keep asking it to finalize the assignment that you have given. Thus, your interaction with ChatGPT will be more efficient if you plan your assignment better.
ChatGPT has limited knowledge of the world after 2021. You need to consider this fact when designing a prompt.
ChatGPT can generate inaccurate responses to questions. You may find several examples below.

Math Item Writing

ChatGPT’s Math skills are not excellent. Below I have outlined some bright examples:

ChatGPT “thinks” that 11/2 equals to 2 and 6/5 equals to 1;
ChatGPT does not realize that one root cannot be a solution of a quadratic equation that has two different roots while it lists both roots among answer options;
ChatGPT makes mistakes when naming a correct response out of a few options generated;
ChatGPT makes distractors (incorrect answers in multiple choice questions) up without considering possible mistakes that test takers can make;
ChatGPT provides a correct solution to a question but does not include it into answer options;
ChatGPT cannot differentiate between levels of difficulty even if it knows the sample from the prompt.

Let’s have a look at a couple of Math questions written by ChatGPT according to my prompts.

Prompt

Response

Comments

ChatGPT, please, write 1 multiple-choice Math question of low difficulty for 16-17 year old high school American students for the following topic in Algebra: Linear equations in two variables. Multiple-choice questions must have only one correct answer (key) and three incorrect answers (distractors) based on test takers’ incorrect reasoning. Please, provide an explanation to each distractor and a solution to the question.

Which point satisfies the equation y = -2x + 7?

A) (5, 1)

B) (-1, 5)

C) (2, 5)

D) (1, -5)

Explanation:

To find a point that satisfies the equation, we can plug in values for x and y that make the equation true. For example, if we let x = 1, then y = -2(1) + 7 = 5. So, the point (1, 5) satisfies the equation.

ChatGPT did not include a correct response into answer options even though it solved this question correctly.

ChatGPT, please, write 1 student-produced response Math question of medium difficulty for 16-17 year old high school American students for the following topic in Algebra: Systems of two linear equations in two variables. Student-produced response questions must have only one correct answer as a real number. Please, provide a solution to this question.

Solve the system of equations:

2x + 3y = 8

x – y = 1.

Explanation: To solve the system of equations, we can use either substitution or elimination method. Using substitution, we can solve one equation for one variable and substitute it into the other equation. For example, we can solve the second equation for x and get x = y + 1. Then, we substitute x = y + 1 into the first equation and get 2(y + 1) + 3y = 8. Simplifying this equation gives us 5y + 2 = 8, which gives us y = 1. Substituting y = 1 into x = y + 1 gives us x = 2. Therefore, the solution to the system of equations is (2, 1).

Solving 5y + 2 = 8 does not give us y = 1 but y = 6/5 or 1.2. Therefore, the point (2, 1) is not a correct solution of this system of equations.

English Item Writing

ChatGPT’s English skills are not as perfect as I thought they would be since it is an English native speaker (I suppose). I have had some experience with ChatGPT making it design several tasks to test students’ English skills. As expected, ChatGPT is more productive with chunks of text. I asked it to make some Reading and Writing questions for SAT and IELTS, and the results were much better than those for Math. However, there were some shortcomings in terms of grammar errors and poor lexical choice. For instance, ChatGPT suggested option A as a correct response that is arguable:

Which of the following options best replaces the underlined phrase in the sentence: “The company’s profits have increased by 20% over the last quarter.”

A) The company’s profits have risen by 20% over the last quarter.

B) The company’s profits have grown by 20% over the last quarter.

C) The company’s profits have expanded by 20% over the last quarter.

D) The company’s profits have developed by 20% over the last quarter.

Russian Item Writing

I was curious how proficient ChatGPT was in Russian. Well, it knows how many letters the Russian alphabet contains and some other facts, but when I asked ChatGPT to write a question to check some grammar, my expectations were betrayed: ChatGPT gave a wrong answer when asked to identify a gerund.

Какое из перечисленных слов является деепричастием?

A) Зеленеющий

B) Шумно

C) Летящий

D) Бежавший

Правильный ответ: C) Летящий – это деепричастие настоящего времени. Ошибочные ответы: A) – прилагательное, B) – наречие, D) – причастие прошедшего времени.

Summary

Since AI-enabled tools do not have real brains and only regurgitate information based on what they “learned” by interacting with billions of data points, it is fair enough that there will be some inaccuracies in their responses even though they will look human-like.

In its own introduction, ChatGPT announced straight away that it was intended to ASSIST us, humans, not to DO things instead of us. ChatGPT can provide helpful responses but is not capable of independent thought or emotions. Like any AI, ChatGPT is vulnerable to multiple issues, including bias and potential inaccuracies.

I would like to re-emphasize once again my point that I outlined in the previous post, that any AI-powered tool no matter how “good” it is needs a smart user to manipulate it. Otherwise, it is going to be a complete disaster! However, if users provide ChatGPT with a very well structured question and the right guidance, there will be a high chance of obtaining an accurate response.

One more thing to consider is that Math is a way of thinking. Therefore, I would not expect AI-powered chatbots to be super-efficient in this field, at least in the near future.

Another important consideration is the legal situation. Current opinion in the field is this: Copyright laws are designed to protect creative development of new things by humans, but if ChatGPT is doing the heavy lifting, then it is not human-created and therefore not fully protected by copyright law. If the items are stolen, you might not have legal recourse. However, this topic is, of course, quite new and continues to evolve.

In the end, is it worth it to use ChatGPT for item writing? Absolutely. Even though there are errors which are quickly found, such as having two correct answers to a question or not having a key specified, these sorts of things are easily fixed. The average time to develop new items can be significantly reduced. Moreover, ChatGPT will continue to get even more powerful! So, stay tuned!

April 9, 2023/by Laila Issayeva M.Sc.

Artificial Intelligence (AI) in Education & Assessment: Opportunities and Best Practices

Laila Issayeva M.Sc.March 19, 2023

Artificial intelligence (AI) is poised to address some challenges that education deals with today, through innovation of teaching and learning processes. By applying AI in education technologies, educators can determine student needs more precisely, keep students more engaged, improve learning, and adapt teaching accordingly to boost learning outcomes. A process of utilizing AI in education started off from looking for a substitute for one-on-one tutoring in the 1970s and has been witnessing multiple improvements since then. This article will look at some of the latest AI developments used in education, their potential impact, and drawbacks they possess.

Application of AI

Recently, a helping hand of AI technologies has permeated into all aspects of educational process. The research that has been going since 2009 shows that AI has been extensively employed in managing, instructing, and learning sectors. In management, AI tools are used to review and grade student assignments, sometimes they operate even more accurately than educators do. There are some AI-based interactive tools that teachers apply to build and share student knowledge. Learning can be enhanced through customization and personalization of content enabled by new technological systems that leverage machine learning (ML) and adaptability.

Below you may find a list of major educational areas where AI technologies are actively involved and that are worthy of being further developed.

Personalized learning	This educational approach tailors learning trajectory to individual student needs and interests. AI algorithms analyze student information (e.g. learning style and performance) to create customized learning paths. Based on student weaknesses and strengths, AI recommends exercises and learning materials. AI technologies are increasingly pivotal in online learning apps, personalizing education and making it more accessible to a diverse learner base.
Adaptive learning	This approach does the same as personalized learning but in real-time stimulating learners to be engaged and motivated. ALEKS is a good example of an adaptive learning program.
Learning courses	These are AI-powered online platforms that are designed for eLearning and course management, and enable learners to browse for specific courses and study with their own speed. These platforms offer learning activities in an increasing order of their difficulty aiming at ultimate educational goals. For instance, advanced Learning Management Systems (LMS) and Massive Open Online Courses (MOOCs).
Learning assistants/Teaching robots	AI-based assistants can supply support and resources to learners upon request. They can respond to questions, provide personalized feedback, and guide students through learning content. Such virtual assistants might be especially helpful for learners who cannot access offline support.
Adaptive testing	This mode of delivering tests means that each examinee will get to respond to specific questions that correspond to their level of expertise based on their previous responses. It is possible due to AI algorithms enabled by ML and psychometric methods, i.e. item response theory (IRT). You can get more information about adaptive testing from Nathan Thompson’s blog post.
Remote proctoring	It is a type of software that allows examiners to coordinate an assessment process remotely whilst keeping confidentiality and preventing examinees from cheating. In addition, there can be a virtual proctor who can assist examinees in resolving any issues arisen during the process. The functionality of proctoring software can differ substantially depending on the stakes of exams and preferences of stakeholders. You can read more on this topic from the ASC’s blog here.
Test assembly	Automated test assembly (ATA) is a widely used valid and efficient method of test construction based on either classical test theory (CTT) or item response theory (IRT). ATA lets you assemble test forms that are equivalent in terms of content distribution and psychometric statistics in seconds. ASC has designed TestAssembler to minimize a laborious and time-consuming process of form building.
Automated grading	Grading student assignments is one of the biggest challenges that educators face. AI-powered grading systems automate this routine work reducing bias and inconsistencies in assessment results and increasing validity. ASC has developed an AI essay scoring system—SmartMarq. If you are interested in automated essay scoring, you should definitely read this post.
Item generation	There are often cases when teachers are asked to write a bunch of items for assessment purposes, as if they are not busy with lesson planning and other drudgery. Automated item generation is very helpful in terms of time saving and producing quality items.
Search engine	The time of libraries has sunk into oblivion, so now we mostly deal with huge search engines that have been constructed to carry out web searches. AI-powered search engines help us find an abundance of information; search results heavily depend on how we formulate our queries, choose keywords, and navigate between different sites. One of the biggest search engines so far is Google.
Chatbot	Last but not least… Chatbots are software applications that employ AI and natural language processing (NLP) to make humanized conversations with people. AI-powered chatbots can provide learners with additional personalized support and resources. ChatGPT can truly be considered as the brightest example of a chatbot today.

Highlights of AI and challenges to address

Today AI-powered functions revolutionize education, just to name a few: speech recognition, NLP, and emotion detection. AI technologies enable identifying patterns, building algorithms, presenting knowledge, sensing, making and following plans, maintaining true-to-life interactions with people, managing complex learning activities, magnifying human abilities in learning contexts, and supporting learners in accordance with their individual interests and needs. AI allows students to use handwriting, gestures or speech as input while studying or taking a test.

Along with numerous opportunities, AI-evolution brings some risks and challenges that should be profoundly investigated and addressed. While approaching utilization of AI in education, it is important to keep caution and consideration to make sure that it is done in a responsible and ethical way, and not to get caught up in the mainstream since some AI tools consult billions of data available to everyone on the web. Another challenge associated with AI is a variability in its performance: some functions are performed on a superior level (such as identifying patterns in data) but some of them are quite primitive (such as inability to support an in-depth conversation). Even though AI is very powerful, human beings still play a crucial role in verifying AI’s output to avoid plagiarism and falsification of information.

Conclusion

AI is already massively applied in education around the world. With the right guidance and frameworks in place, AI-powered technologies can help build more efficient and equitable learning experiences. Today we have an opportunity to witness how AI- and ML-based approaches contribute to development of individualized, personalized, and adaptive learning.

ASC’s CEO, Dr Thompson, presented several topics on AI at the 2023 ATP Conference in Dallas, TX. If you are interested in utilizing AI-powered services provided by ASC, please do not hesitate to contact us!

References

Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: A guidance for policymakers. UNESCO.

Niemi, H., Pea, R. D., & Lu, Y. (Eds.). (2022). AI in learning: Designing the future. Springer. https://doi.org/10.1007/978-3-031-09687-7

March 19, 2023/by Laila Issayeva M.Sc.