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What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis

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What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis

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A peer-reviewed journal article published in Frontiers in Psychology.

February 2, 2022.

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What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis

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Procrastination is generally perceived as a common behavioral tendency, and there are a growing number of literatures to discuss this complex phenomenon. To elucidate the overall perspective and keep abreast of emerging trends in procrastination…

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Why We Procrastinate: The Psychology of Putting Things Off

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Why We Procrastinate: The Psychology of Putting Things Off

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A mental health article published on McLean Hospital's website.

July 13, 2025

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The Real Reason You're Procrastinating | McLean Hospital

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Procrastination seems like it's a normal behavior, but sometimes can point to a mental health issue. Learn the difference between the two and if you may need help.

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Overcoming Procrastination Via Generative AI

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Overcoming Procrastination Via Generative AI

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An opinion article published on Forbes.com.

September 1, 2024.

Forbes

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Overcoming Procrastination Via Generative AI

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We all procrastinate from time to time. It is human nature. Turns out that generative AI can help cope with your procrastination. Here’s the scoop on how.

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Time perspectives and procrastination in university students: exploring the moderating role of basic psychological need satisfaction

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Time perspectives and procrastination in university students: exploring the moderating role of basic psychological need satisfaction

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A peer-reviewed research article published in the journal BMC Psychology.

January 2, 2024.

BMC Psychology
"Time perspectives and procrastination in university students: exploring the moderating role of basic psychological need satisfaction"

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Time perspectives and procrastination in university students: exploring the moderating role of basic psychological need satisfaction - BMC Psychology

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Background Research on procrastination, regarding time perspective factors and basic psychological need satisfaction (BPNS) has placed this problem at the meeting point of individual and contextual variables. The present study focused both on the individual, given that time perspectives can be defined as a person’s attitude to an object (time) at three moments (present, past, future); and on contextual aspects, because the satisfaction of basic psychological needs (competence, autonomy, relatedness) is facilitated or made difficult by social contexts. Based on this, the aim of this study was to analyse the relationships between time perspectives and inter-subject procrastination variations, testing the moderating role of BPNS in this relationship. Method A total of 1,188 undergraduate students, aged 17–50 years (M = 20.02, SD = 2.63), completed three questionnaires containing the variables of interest. Results Regression analyses showed significant negative (thus, potentially protective) association of future time perspective with all three procrastination dimensions (decisional procrastination, implemental delay, and lateness). Conversely, past-negative time perspective showed a positive (thus, potentially adverse) association with procrastination. Satisfaction of the need for competence also showed a negative (thus, potentially protective) association with all procrastination dimensions. On eight occasions, the relationships between time perspectives and procrastination dimensions were moderated by psychological need satisfaction. Conclusions These findings show that BPNS may play relevant roles in the negative (favourable) relationships between procrastination dimensions and positive time perspectives, as well as in the positive (adverse) associations between negative time perspectives and procrastination dimensions. Contextual interventions fostering enhanced levels of perceived autonomy, competence, and relatedness, as well as future time perspective, are thus strong candidates to consider for use and evaluation by policy makers, pedagogues, teachers, coaches and other professionals interested in counteracting procrastination tendencies.

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Personalized Learning through Artificial Intelligence: Opportunities, Risks, and Policy Perspectives.

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Personalized Learning through Artificial Intelligence: Opportunities, Risks, and Policy Perspectives.

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Article

Stošić, Lazar;Radonjić, Aleksandar;Krčadinac, Olja;

2025

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How to Break Out of Your Pandemic Procrastination Rut

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How to Break Out of Your Pandemic Procrastination Rut

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A wellness article published on The Cut (a New York Magazine publication).

June 29, 2021.

The Cut

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How to Break Out of Your Pandemic Procrastination Rut

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Many of us are procrastinating more than ever. How do we break the habit?

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How to Stop Procrastinating: 10 Practical Procrastination Tips

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How to Stop Procrastinating: 10 Practical Procrastination Tips

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Website article

January 2024

Coursera

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How to Stop Procrastinating: 10 Practical Tips

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Learn how to recognize when you are procrastinating and explore methods that can help you stop.

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Procrastination: A Scientific Guide on How to Stop Procrastinating

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Procrastination: A Scientific Guide on How to Stop Procrastinating

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Website article/blog post

Originally 2016, updated 2023

JamesClear

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Procrastination is a challenge we have all faced at one point or another. For as long as humans have been around, we have been struggling with delaying, avoiding, and procrastinating on issues that matter to us.

During our more productive moments, when we temporarily figure out how to stop procrastinating, we feel satisfied and accomplished. Today, we're going to talk about how to make those rare moments of productivity more routine. The purpose of this guide is to break down the science behind why we procrastinate, share proven frameworks you can use to beat procrastination, and cover useful strategies that will make it easier to take action.

You can click the links below to jump to a particular section or simply scroll down to read everything. At the end of this page, you'll find a complete list of all the articles I have written on procrastination.
I. The Science Behind Procrastination II. How to Stop Procrastinating Right Now

1. Make the Rewards of Taking Action More Immediate

2. Make the Consequences of Procrastination More Immediate

3. Design Your Future Actions

4. Make the Task More Achievable

III. Being Consistent: How to Kick the Procrastination Habit

1. The Daily Routine Experts Recommend for Peak Productivity

2. How to Avoid Chronic Procrastination With Visual Cues

The Science Behind Procrastination

Let's start by getting the basics nailed down. What is procrastination? What does procrastination mean? What exactly are we dealing with here?

What is Procrastination?

Human beings have been procrastinating for centuries. The problem is so timeless, in fact, that ancient Greek philosophers like Socrates and Aristotle developed a word to describe this type of behavior: Akrasia.

Akrasia is the state of acting against your better judgment. It is when you do one thing even though you know you should do something else. Loosely translated, you could say that akrasia is procrastination or a lack of self-control.

Here's a modern definition:

Procrastination is the act of delaying or postponing a task or set of tasks. So, whether you refer to it as procrastination or akrasia or something else, it is the force that prevents you from following through on what you set out to do.

Why Do We Procrastinate?

Ok, definitions are great and all, but why do we procrastinate? What is going on in the brain that causes us to avoid the things we know we should be doing?

This is a good time to bring some science into our discussion. Behavioral psychology research has revealed a phenomenon called "time inconsistency," which helps explain why procrastination seems to pull us in despite our good intentions. Time inconsistency refers to the tendency of the human brain to value immediate rewards more highly than future rewards. 1

The best way to understand this is by imagining that you have two selves: your Present Self and your Future Self. When you set goals for yourself - like losing weight or writing a book or learning a language - you are actually making plans for your Future Self. You are envisioning what you want your life to be like in the future. Researchers have found that when you think about your Future Self, it is quite easy for your brain to see the value in taking actions with long-term benefits. The Future Self values long-term rewards.

However, while the Future Self can set goals, only the Present Self can take action. When the time comes to make a decision, you are no longer making a choice for your Future Self. Now you are in the present moment, and your brain is thinking about the Present Self. Researchers have discovered that the Present Self really likes instant gratification, not long-term payoff.

So, the Present Self and the Future Self are often at odds with one another. The Future Self wants to be trim and fit, but the Present Self wants a donut. Sure, everyone knows you should eat healthy today to avoid being overweight in 10 years. But consequences like an increased risk for diabetes or heart failure are years away.

Similarly, many young people know that saving for retirement in their 20s and 30s is crucial, but the benefit of doing so is decades off. It is far easier for the Present Self to see the value in buying a new pair of shoes than in socking away $100 for 70-year-old you. (If you're curious, there are some very good evolutionary reasons for why our brain values immediate rewards more highly than long-term rewards.)

This is one reason why you might go to bed feeling motivated to make a change in your life, but when you wake up you find yourself falling back into old patterns. Your brain values long-term benefits when they are in the future (tomorrow), but it values immediate gratification when it comes to the present moment (today).

The Procrastination-Action Line

You cannot rely on long-term consequences and rewards to motivate the Present Self. Instead, you have to find a way to move future rewards and punishments into the present moment. You have to make the future consequences become present consequences.

This is exactly what happens during the moment when we finally move beyond procrastination and take action. For example, let's say you have a report to write. You've known about it for weeks and continued to put it off day after day. You experience a little bit of nagging pain and anxiety thinking about this paper you have to write, but not enough to do anything about it. Then, suddenly, the day before the deadline, the future consequences turn into present consequences, and you write that report hours before it is due. The pain of procrastinating finally escalated and you crossed the "Action Line."

There is something important to note here. As soon as you cross the Action Line, the pain begins to subside. In fact, being in the middle of procrastination is often more painful than being in the middle of doing the work. Point A on the chart above is often more painful than Point B. The guilt, shame, and anxiety that you feel while procrastinating are usually worse than the effort and energy you have to put in while you're working. The problem is not doing the work, it's starting the work. 2

If we want to stop procrastinating, then we need to make it as easy as possible for the Present Self to get started and trust that motivation and momentum will come after we begin. ( Motivation often comes after starting, not before.)

Let's talk about how to do that now.

How to Stop Procrastinating Right Now

There are a variety of strategies we can employ to stop procrastinating. Below, I'll outline and explain each concept, then I'll provide you with some examples of strategy in action.

Option 1: Make the Rewards of Taking Action More Immediate

If you can find a way to make the benefits of long-term choices more immediate, then it becomes easier to avoid procrastination. One of the best ways to bring future rewards into the present moment is with a strategy known as temptation bundling.

Temptation bundling is a concept that came out of behavioral economics research performed by Katy Milkman at The University of Pennsylvania. Simply put, the strategy suggests that you bundle a behavior that is good for you in the long-run with a behavior that feels good in the short-run.

The basic format is: Only do [THING YOU LOVE] while doing [THING YOU PROCRASTINATE ON].

Here are a few common examples of temptation bundling:

Only listen to audiobooks or podcasts you love while exercising.

Only get a pedicure while processing overdue work emails.

Only watch your favorite show while ironing or doing household chores.

Only eat at your favorite restaurant when conducting your monthly meeting with a difficult colleague.

This article covers some specific exercises you can follow to figure out how to create temptation bundling ideas that work for you.

Option 2: Make the Consequences of Procrastination More Immediate

There are many ways to force you to pay the costs of procrastination sooner rather than later. For example, if you are exercising alone, skipping your workout next week won't impact your life much at all. Your health won't deteriorate immediately because you missed that one workout. The cost of procrastinating on exercise only becomes painful after weeks and months of lazy behavior. However, if you commit to working out with a friend at 7 a.m. next Monday, then the cost of skipping your workout becomes more immediate. Miss this one workout and you look like a jerk.

Another common strategy is to use a service like Stickk to place a bet. If you don't do what you say you'll do, then the money goes to a charity you hate. The idea here is to put some skin in the game and create a new consequence that happens if you don't do the behavior right now.

Option 3: Design Your Future Actions

One of the favorite tools psychologists use to overcome procrastination is called a "commitment device." Commitment devices can help you stop procrastinating by designing your future actions ahead of time.

For example, you can curb your future eating habits by purchasing food in individual packages rather than in the bulk size. You can stop wasting time on your phone by deleting games or social media apps. (You could also block them on your computer.)

Similarly, you can reduce the likelihood of mindless channel surfing by hiding your TV in a closet and only taking it out on big game days. You can voluntarily ask to be added to the banned list at casinos and online gambling sites to prevent future gambling sprees. You can build an emergency fund by setting up an automatic transfer of funds to your savings account. These are all examples of commitment devices that help reduce the odds of procrastination.

Option 4: Make the Task More Achievable

As we have already covered, the friction that causes procrastination is usually centered around starting a behavior. Once you begin, it's often less painful to keep working. This is one good reason to reduce the size of your habits because if your habits are small and easy to start, then you will be less likely to procrastinate.

One of my favorite ways to make habits easier is to use The 2-Minute Rule, which states, "When you start a new habit, it should take less than two minutes to do." The idea is to make it as easy as possible to get started and then trust that momentum will carry you further into the task after you begin. Once you start doing something, it's easier to continue doing it. The 2-Minute Rule overcomes procrastination and laziness by making it so easy to start taking action that you can't say no.

Another great way to make tasks more achievable is to break them down. For example, consider the remarkable productivity of the famous writer Anthony Trollope. He published 47 novels, 18 works of non-fiction, 12 short stories, 2 plays, and an assortment of articles and letters. How did he do it? Instead of measuring his progress based on the completion of chapters or books, Trollope measured his progress in 15-minute increments. He set a goal of 250 words every 15 minutes and he continued this pattern for three hours each day. This approach allowed him to enjoy feelings of satisfaction and accomplishment every 15 minutes while continuing to work on the large task of writing a book.

Making your tasks more achievable is important for two reasons.

1. Small measures of progress help to maintain momentum over the long-run, which means you're more likely to finish large tasks.

2. The faster you complete a productive task, the more quickly your day develops an attitude of productivity and effectiveness. 3

I have found this second point, the speed with which you complete your first task of the day, to be of particular importance for overcoming procrastination and maintaining a high productive output day after day.

Being Consistent: How to Kick the Procrastination Habit

Alright, we've covered a variety of strategies for beating procrastination on a daily basis. Now, let's discuss some ways to make productivity a long-term habit and prevent procrastination from creeping back into our lives.

The Daily Routine Experts Recommend for Peak Productivity

One reason it is so easy to slip back into procrastination time after time is because we don't have a clear system for deciding what is important and what we should work on first. (This is yet another example of the system often being more important than the goal.)

One of the best productivity systems I have found is also one of the most simple. It's called The Ivy Lee Method and it has six steps:

1. At the end of each work day, write down the six most important things you need to accomplish tomorrow. Do not write down more than six tasks.

2. Prioritize those six items in order of their true importance.

3. When you arrive tomorrow, concentrate only on the first task. Work until the first task is finished before moving on to the second task.

4. Approach the rest of your list in the same fashion. At the end of the day, move any unfinished items to a new list of six tasks for the following day.

5. Repeat this process every working day.

Here's what makes it so effective:

It's simple enough to actually work. The primary critique of methods like this one is that they are too basic. They don't account for all of the complexities and nuances of life. What happens if an emergency pops up? What about using the latest technology to our fullest advantage? In my experience, complexity is often a weakness because it makes it harder to get back on track. Yes, emergencies and unexpected distractions will arise. Ignore them as much as possible, deal with them when you must, and get back to your prioritized to-do list as soon as possible. Use simple rules to guide complex behavior.

It forces you to make tough decisions. I don't believe there is anything magical about Lee's number of six important tasks per day. It could just as easily be five tasks per day. However, I do think there is something magical about imposing limits upon yourself. I find that the single best thing to do when you have too many ideas (or when you're overwhelmed by everything you need to get done) is to prune your ideas and trim away everything that isn't absolutely necessary. Constraints can make you better. Lee's method is similar to Warren Buffett's 25-5 Rule, which requires you to focus on just five critical tasks and ignore everything else. Basically, if you commit to nothing, you'll be distracted by everything.

It removes the friction of starting. The biggest hurdle to finishing most tasks is starting them. (Getting off the couch can be tough, but once you actually start running it is much easier to finish your workout.) Lee's method forces you to decide on your first task the night before you go to work. This strategy has been incredibly useful for me: as a writer, I can waste three or four hours debating what I should write about on a given day. If I decide the night before, however, I can wake up and start writing immediately. It's simple, but it works. In the beginning, getting started is just as important as succeeding at all.

It requires you to single-task. Modern society loves multi-tasking. The myth of multi-tasking is that being busy is synonymous with being better. The exact opposite is true. Having fewer priorities leads to better work. Study world-class experts in nearly any field-athletes, artists, scientists, teachers, CEOs-and you'll discover one characteristic runs through all of them: focus. The reason is simple. You can't be great at one task if you're constantly dividing your time ten different ways. Mastery requires focus and consistency.

Regardless of what method you use, the bottom line is this: Do the most important thing first each day and let the momentum of the first task carry you into the next one.

How to Avoid Chronic Procrastination With Visual Cues

Another way to overcome the trap of chronic procrastination is to use visual cues to trigger your habits and measure your progress.

A visual cue is something you can see (a visual reminder) that prompts you to take action. Here's why they are important for beating procrastination:

Visual cues remind you to start a behavior. We often lie to ourselves about our ability to remember to perform a new habit. ( "I'm going to start eating healthier. For real this time.") A few days later, however, the motivation fades and the busyness of life begins to take over again. Hoping you will simply remember to do a new habit is usually a recipe for failure. This is why a visual stimulus can be so useful. It is much easier to stick with good habits when your environment nudges you in the right direction.

Visual cues display your progress on a behavior. Everyone knows consistency is an essential component of success, but few people actually measure how consistent they are in real life. Having a visual cue-like a calendar that tracks your progress-avoids that pitfall because it is a built-in measuring system. One look at your calendar and you immediately have a measure of your progress.

Visual cues can have an additive effect on motivation. As the visual evidence of your progress mounts, it is natural to become more motivated to continue the habit. The more visual progress you see, the more motivated you will become to finish the task. There are a variety of popular behavioral economics studies that refer to this as the Endowed Progress Effect. Seeing your previous progress is a great way to trigger your next productive action.

Two of my favorite strategies that use visual cues are The Paper Clip Strategy, which is helpful for beating procrastination day-after-day, and The Seinfeld Strategy, which is great for maintaining consistency over longer periods of time.

Where to Go From Here

I hope you found this short guide on procrastination useful. If you're looking for more ideas on how to stop procrastinating and take action, then check out my full list of procrastination articles below.

All Procrastination Articles

This is a complete list of articles I have written on procrastination. Enjoy!

Best Articles on Related Topics

Or, browse my best articles.

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Procrastination: A Scientific Guide on How to Stop Procrastinating

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This guide explains what procrastination is, how it works, and how to stop procrastinating. Now for your first test: Don't procrastinate, and read this now.

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Website article

December 2023 (originally Jan 10 2024)

Boston Consulting Group (BCG) website

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Higher education needs to incorporate AI in two ways: to support and instruct students, and as part of the curriculum to prepare students to use AI in their work.

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Artificial intelligence in higher education: the state of the field

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Artificial intelligence in higher education: the state of the field

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Research article / peer-reviewed journal.

Published 24 April 2023.

International Journal of Educational Technology in Higher Education

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Artificial intelligence in higher education: the state of the field - International Journal of Educational Technology in Higher Education

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This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

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Research article / peer-reviewed open-access journal

Published 28 October 2019.

International Journal of Educational Technology in Higher Education

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Systematic review of research on artificial intelligence applications in higher education – where are the educators? - International Journal of Educational Technology in Higher Education

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Artificial intelligence (AI) learning tools in K-12 education: A scoping review

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Artificial intelligence (AI) learning tools in K-12 education: A scoping review

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Research article / peer-reviewed open-access journal.

Published 06 January 2024.

Journal of Computers in Education

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Artificial intelligence (AI) learning tools in K-12 education: A scoping review

Abstract

Artificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students' learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google's Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human-computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students' learning outcomes. Research implications and future research directions within the K-12 context are also discussed.

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Introduction

Artificial intelligence (AI) was defined in 1956 as "the science and engineering of creating intelligent machines" (McCarthy, 2004, p.2). AI education is considered a driver of economic growth, future workforce development, and global competitiveness (Cetindamar et al., 2022; Sestino & De Mauro, 2022). Researchers' interest in equipping students with AI knowledge, skills, and attitudes to thrive in an AI-rich future (Miao et al., 2021; Rina et al., 2022; Wang & Cheng, 2021) has given rise to the term "AI literacy", which concerns the design and implementation of AI learning activities, learning tools and applications, and pedagogical models. Some educators focus on demonstrating machine learning through activities for mastering coding skills and AI concepts (Marques et al., 2020), while others suggest focusing on computational thinking and engagement in deductive and logical reasoning practices (Wong, 2020). In this paper, it is argued that AI education should be extended beyond universities to K-12 students.

There have been a number of recent studies of AI in the context of kindergartens (Su & Yang, 2022; Williams et al., 2019a, 2019b), primary schools (Ali et al., 2019; Shamir & Levin, 2021), and secondary schools (Norouzi et al., 2020; Yoder et al., 2020). However, little is known about what and how AI should be taught (Su et al., 2023a; Ng et al., 2023; Van Brummelen et al., 2021). One challenge is delivering AI content in an age-appropriate and effective manner (Su et al., 2023b; Su & Yang, 2023). Despite the numerous AI learning tools available in K-12 contexts (Rizvi et al., 2023; Van Brummelen et al., 2021), such as Turtle Robot (Papert & Solomon, 1971), PopBots (Williams et al., 2019a) and LearningML applications (Rodríguez-García et al., 2020), many educators are concerned about the suitability of these tools (Chiu & Chai, 2020; Su & Yang, 2023).

Theoretical model

Modified technology-based learning model by the researchers of this review (adopted from Hsu et al., 2012)

According to Hsu et al. ( 2012), "research methods", "research issues", and "application domains" are the three main categories to be considered in the development of a coding scheme to gauge research trends in the field of technology-based learning and education. In terms of research methods, a quantitative, qualitative, and mixed approach is employed in this study to construct the coding scheme for the review of the literature (McMillan & Schumacher, 2010). In terms of research issues, with reference to Chang et al. ( 2018), learning outcomes are categorized as cognitive, affective, behavioral, and skills acquisition outcomes. Finally, two application domains are pursued in this paper: (1) the pedagogical strategies commonly used in science courses, which were employed by Lai and Hwang ( 2015) and which include constructive, reflective, didactic, and unplugged pedagogies (Cope & Kalantzis, 2016), and (2) the learning tools, namely, hardware, software, intelligent agents, and unplugged strategies, which are coded as suggested by Ng and Chu ( 2021).

Research objectives

In this study, the literature on pedagogical strategies, assessment methods, learning tools, and learning outcomes in AI K-12 settings is studied. Four research questions are formulated.

RQ1: What are the potential learning tools identified in AI K-12 education?

RQ2: What pedagogical strategies are commonly proposed by studies on AI K-12 learning tools?

RQ3: What learning outcomes have been demonstrated in studies on AI K-12 learning tools?

RQ4: What are the research and assessment methods used in studies on AI K-12 learning tools?

Methods

This study follows the same four steps employed in other studies on AI literacy in K-12 (e.g., Ng et al., 2022; Su et al., 2022): (1) identifying relevant studies, (2) selecting and excluding eligible studies, (3) data analysis, and (4) reporting findings. In this study, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (Moher et al., 2015) are followed.

Identifying relevant studies

The electronic databases used for the literature search were ACM, EBSCO, Web of Science, and Scopus. The aim of this review is to provide a comprehensive K-12 education for learning tools, encompassing early childhood education and primary and secondary education. As the education systems of different countries may differ from each other, the search string used in the paper for K-12 includes from kindergarten to secondary school students. In addition, learning tools are defined as a variety of learning platforms and systems, educational applications and activities that can enhance the teaching process and support students in AI literacy learning. Therefore, the search strings are reflected with specific definitions for K-12 and learning tools to search for target articles and data, as shown in Table 1.

Study selection and exclusion

To ensure the generalizability of the findings and to avoid biases in article selection, specific inclusion and exclusion criteria are employed in this study (Table 2).

The snowball method

To enhance the systematic search for relevant literature, the snowballing method as outlined by Sayers ( 2008) was employed. This involved tracing references in previously selected articles. The focus was on the references cited in the earlier selected articles as discovered through Google Scholar. Utilizing the snowballing method led to the identification of three additional articles that met the eligibility criteria described above.

Overview of selected studies

Table 3 presents an overview of the 46 selected studies, including the type of articles, year of publication, and educational level.

Publication trends

Forty-six articles were identified: 28 conference papers and 18 journal articles. The first article was published in 1995, and 39 articles have been published in the past 5 years, with a peak in 2021 (Fig. 3).

Countries

Most research took place in the USA (N = 8), China (N = 7), Finland (N = 3), Hong Kong (N = 3), Israel (N = 3), Spain (N = 3), Australia (N = 2) and Japan (N=2). Others were conducted in Brazil, Denmark, Greece, Indonesia, New Zealand, Norway, Sweden, Thailand, and the UK. The locations of the remaining six articles are unknown.

Educational levels

Primary and secondary schools are both the most researched educational levels, each covering 44% of the selected articles, followed by kindergartens (11%) and K-12 education (2%).

These selected studies generally include samples of students of both genders and a wide range of ages, from 3-year-old kindergarten students (Vartiainen et al., 2020) to 20-year-old Danish high school students (Kaspersen et al., 2021). It also encompasses participants in science technology engineering mathematics (STEM) classes (Ho et al., 2019), high-performing students of the Scientists in School program (Heinze et al., 2010), students with and without an AI background (Yoder et al., 2020), and students from varying socioeconomic backgrounds (Kaspersen et al., 2021).

There were three AI-related research studies between 1995 and 2017, mostly adopting unplugged activities and games for AI teaching, which are different from research conducted after 2017. The first article was published by Scherz and Haberman ( 1995), who designed a special AI curriculum with the use of abstract data types and instructional models (e.g., graphs and decision trees) to teach AI concepts such as logic programming and AI systems to high school students in Israel. In another two studies, the use of programming robots (Heinze et al., 2010) and computer science unplugged activities (Lucas, 2009) were explored with Australian and New Zealand K-6 students, respectively. Since then, a greater variety of learning tools have been employed and expanded to European and Asian countries across all educational levels in K-12 settings. Appendix 1 provides an overview of the selected articles.

Findings

RQ1: What are the potential learning tools identified in AI K-12 education?

The potential learning tools identified in K-12 contexts were intelligent agents (N = 20), software-focused devices (N = 19), hardware-focused devices (N = 10), and unplugged activities (N = 6) (Fig. 4 and Table 4). In this section, intelligent agents, software devices, and hardware devices are discussed.

Intelligent agents

In addition, Learning ML has been employed for primary schools to create AI-driven solutions and models, for example, to teach the supervised machine learning principle (Voulgari et al., 2021; Rodríguez-Garciá et al., 2021), which simplifies abstract AI algorithms for primary school students. Machine Learning for Kids, which introduces the power of the IBM Watson engine for AI modelling (Fernández-Martínez et al., 2021), Cognimates (Sabuncuoglu, 2020; Fernández-Martínez et al., 2021), which allows students to practice coding, and Ecraft2Learn, which contains a deep learning functionality (Kahn et al., 2018), have also been used in secondary school classrooms. Intelligent agents often offer students hands-on experience to develop datasets and to build customized machine learning systems.

Software devices

Software devices are adopted to enable mostly primary and secondary school students to learn about computational thinking, including programming for sequences, rule-based and conditional mechanisms, as well as data science and machine learning using visual language. For example, Scratch, a block-based programming software, is frequently used in both primary (Dai et al., 2023; Li & Song, 2019; Shamir & Levin, 2021) and secondary schools (Estevez et al., 2019; Fernández-Martínez et al., 2021). Other software is used for visualizing and scaffolding abstract AI concepts through online games and experiences, such as Quick and Draw (Martins et al., 2023) and Music Box (Han et al., 2018). In primary schools, Kitten is used to teach block-based programming (Li & Song, 2019), whereas C++ and JavaScript are used for logical thinking and simulation (Gong et al., 2020). In secondary schools, researchers have often employed free online software and tools, such as Snap (Yoder et al., 2020) and Python (Gong et al., 2018; Norouzi et al., 2020), for algorithm automation, as well as RapidMiner for no-code data science learning (Sakulkueakulsuk et al., 2018). To introduce machine learning concepts to secondary school students, other researchers have focused on developing online games such as the Rock Paper Game (Kajiwara et al., 2023) and the 3D role-player video game Quest (Priya et al., 2022).

Hardware devices

In addition, hardware, such as robotics and physical artifacts, has also been used with built-in software to supplement students' understanding of AI concepts. Williams et al. ( 2019a, 2019b) introduced a preschool originated programming platform consisting of a social robot (PopBot) and a block-based programming interface. In Williams et al. ( 2019a), 80 prekindergartens to second-grade children (aged four to seven) were asked to build their own LEGO robot characters by using DUPLO block programming. PopBot is used as a learning companion to demonstrate its human-like behavior and to demystify AI concepts to younger students.

The lawn bowling robot (Ho et al., 2019), Zhorai conversational robot (Lin et al., 2020), Micro: Bits (Lin et al., 2021), and Plush toys (Tseng et al., 2021) have been used in primary schools, while CUHKiCar (Chiu et al., 2021), the Alpha robot dog (Chai et al., 2020), Raspberry Pi Raspbian and a four-wheel drive chassis (Gong et al., 2018) have been used in secondary schools. For example, in Ho et al. ( 2019), grade six students built lawn-bowling robots for games and competitions while learning about the binary search and optimization algorithms of machine learning. Chiu et al. ( 2021) introduced the robotic CUHKiCar to secondary school students so that they could perform face-tracking and line following tasks.

RQ2: What pedagogical strategies are commonly proposed by studies on AI K-12 learning tools?

As shown in Fig. 5, the four orientations of pedagogy are summarized as authentic/constructive, reflective, didactic, and unplugged. While a total of 17 potential pedagogical strategies were identified within the four orientations in K-12 contexts (Table 5), authentic/constructive methodologies with project-based learning (N = 27) were the most popular pedagogy used across kindergartens (Williams et al., 2019a, 2019b), primary schools (Toivonen et al., 2020; Rodríguez-Garciá et al., 2021), and secondary schools (Gong et al., 2018; Kilhoffer et al., 2023; Sakulkueakulsuk et al., 2018). When teaching AI to students with a diverse range of needs, the evidence demonstrates the positive impact of combining multiple pedagogical approaches in K-12 studies (Heinze et al., 2010; Lee et al., 2021; Williams et al., 2019a, 2019b).

First, authentic and constructive methodologies, project-based (N = 27), human-computer interaction (N = 7), and play-based active learning (N = 5) approaches have been commonly used in K-12 education. Offering hands-on opportunities to students to learn about real-world applications of AI is an example of project-based learning (Fernández-Martínez et al., 2021; Han et al., 2018; Williams et al., 2019a). Other researchers have examined whether students can acquire AI knowledge on human-computer interactive experiences and have found that this does not require any prior knowledge of AI models, such as Zohari (Melsión et al., 2021) and Google Teachable Machine (Lin et al., 2020; Vartiainen et al., 2020). In addition, child-centered play-based learning can effectively engage students and encourage them to take the initiative to construct knowledge during the process of imaginative play (Heinze et al., 2010), which involves students adopting the role of AI developer, tester, and AI robot (Henry et al., 2021).

Pedagogical strategies in kindergartens

Pedagogical strategies in primary schools

Project-based learning is more frequently used in primary schools than in kindergartens: It has been reported as a learning approach in 14 of the 18 studies of primary school settings, compared to only three of the five studies in the kindergarten setting. Similarly, in primary school settings, studies have revealed a strong dependence on play/game-based (N = 5) and human-computer interaction learning approaches (N = 3).

Pedagogical strategies in secondary schools

Second, collaborative learning allows students to work in groups to promote cognitive knowledge, as it engages them in scientific inquiry with the help of smart devices (Wan et al., 2020). Kaspersen et al. ( 2021) designed a collaborative learning tool, VotestratesML, together with a voting project allowing students to build machine learning models based on real-world voting data to predict results.

RQ3: What learning outcomes have been demonstrated in studies on AI K-12 learning tools?

Of the 46 articles, 31 reported potential learning outcomes: (1) cognitive outcomes, (2) affective and behavioral outcomes, and (3) the level of course satisfaction and soft skills acquisition.

Cognitive outcomes

Thirty-one studies documented various degrees of positive cognitive outcomes. Students generally showed a basic understanding of AI, including AI rule-based systems (Ho et al., 2019), machine learning principles and applications (Han et al., 2018; Shamir & Levin, 2021), AI ethics (Melsión et al., 2021), and AI limitations (Lin et al., 2020). In Williams et al. ( 2019a), 70% of prekindergarten and kindergarten students understood knowledge-based systems, whereas Vartiainen et al. ( 2020) found that, through AI learning tools, younger students developed their computational thinking and their understanding of machine-learning principles and applications. Then, Dai et al. ( 2023) reported that primary school students taught with analogy-based pedagogy (i.e., using humans as a reference to teach and learn AI) significantly outperformed primary school students taught with the conventional direct instructional approach in terms of developing their conceptual understanding and increasing their AI technical knowledge proficiency as well as their ethical awareness of AI. Other researchers have argued that primary school students have demonstrated their understanding of AI by constructing and applying machine-learning algorithms with the help of digital role-playing games (Voulgari et al., 2021) and project-based pedagogy (Shamir & Levin, 2021). Through designing and programming a robot, students increased their understanding of AI biases (Melsión et al., 2021). In secondary schools, researchers have also reported an increase in students' knowledge of AI algorithms (Yoder et al., 2020) and machine learning concepts (Sakulkueakulsuk et al., 2018), as well as their recognition of AI patterns (Wan et al., 2020). For example, students understood the fundamental neural networks of machine learning concepts by developing a classification model of recycling images (Martins et al., 2023).

Affective and behavioral outcomes

Affective and behavioral outcomes have been identified in AI learning tool studies within K-12 contexts. In general, students' motivation to learn AI (Han et al., 2018; Shamir & Levin, 2021, 2022) and their interest in the course (Mariescu-Istodor & Jormanainen, 2019; Martins et al., 2023) were enhanced as a result of AI learning activities. Students' perceptions of the relevance of AI to their life also increased (Kajiwara et al., 2023; Lin et al., 2021). Students scored high on self-efficacy (Kajiwara et al., 2023; Shamir & Levin, 2022) and confidence (Shamir & Levin, 2021) in training and validating an AI system. In Martins et al. ( 2023), over 45% of 108 secondary school student participants in the introductory course "Machine Learning for all" reported that they perceived AI learning as an enjoyable experience, and 63% of them hoped to learn more about machine learning in the future.

Moreover, students reported that they were highly motivated to explore the Teachable Machine (Vartiainen et al., 2020), to design the robotic arm and computer source codes (Ho et al., 2019), to draw animals and sea creatures for the machine learning project (Mariescu-Istodor & Jormanainen, 2019), and to predict the sweetness of mangoes by using machine learning models (Sakulkueakulsuk et al., 2018).

From the behavioral perspective, high student engagement was reported in project-based (Kaspersen et al., 2021; Shamir & Levin, 2021; Wan et al., 2020) and play/game-based (Heinze et al., 2010; Voulgari et al., 2021) settings. Primary students attended all sessions and expressed a desire to join an upcoming AI contingency course (Shamir & Levin, 2021), while secondary students were actively engaged in scientific inquiry (Wan et al., 2020). Students were also keen on recommending AI games to their friends (Voulgari et al., 2021). Therefore, a combination of play/game-based and project-based approaches may consolidate AI concepts through gameplay while enhancing students' engagement in AI projects (Han et al., 2018).

Level of satisfaction and soft skills acquisition

The findings from the selected articles reveal that a deep understanding of AI promotes the acquisition of various soft skills. Ali et al. ( 2019) found that students' intellectual curiosity increased after engaging in the construction of an AI neuron. By using bulletin boards shared electronically and online chats for feedback, their collaboration and communication skills were also enhanced (Shamir & Levin, 2021). Moreover, students reported gaining problem solving and technical skills when working with AI systems, including coding, designing simple algorithms, and debugging in Scratch learning activities (Dai et al., 2023).

RQ4: What were the research and assessment methods used in AI K-12 learning tools studies?

In this section, an overview is presented of research methods and data collection procedures within K-12 contexts. Overall, researchers adopted a mixed method (N = 19), qualitative (N = 15) and quantitative methods (N = 12) in AI learning tools in K-12 research. Mixed methods are predominantly used in both primary school (e.g., Dai et al., 2023; Martins et al., 2023; Shamir & Levin, 2021; Toivonen et al., 2020) and secondary school contexts (e.g., Chiu et al., 2021; Estevez et al., 2019), whereas qualitative methods are commonly used in kindergartens (e.g., Heinze et al., 2010; Vartiainen et al., 2020), as shown in Table 6.

A variety of assessment methods were used: questionnaires and surveys (N = 30), artifacts/performance-based evaluation (N = 15), interviews (N = 14), observations (N = 5), games assessment (N = 1), and field visits (N = 1) (Table 7). The two most commonly used data collection methods - questionnaires and surveys and artifacts/performance-based evaluation - are discussed in this section.

In terms of assessment methods, questionnaires and surveys (N = 30) and artifacts/performance-based evaluation (N = 17) are the two most commonly used data collection methods across K-12 contexts (Table 7).

Questionnaires and surveys are used in a quantitative methodology to understand the perception of robotics and theory of mind (e.g., knowledge access, content false belief and explicit false belief). For example, perception of robotics and theory of mind were used in kindergartens (Williams et al., 2019a, 2019b).

Surveys were used to evaluate primary school students' motivation (Lin et al., 2021), self-efficacy in AI learning (Shamir & Levin, 2022), and perceived knowledge and competence (Dai et al., 2023; Mariescu-Istodor & Jormanainen, 2019; Ng et al., 2022). In addition to Ali et al. ( 2019), who used the Torrance test for assessment, researchers also utilized pre- and posttests (Tseng et al., 2021) to compare the AI learning outcomes of control and treatment groups in primary school settings (Melsión et al., 2021). Others provided AI educational experience without stating the assessment method (Ho et al., 2019; Lee et al., 2020; Tseng et al., 2021). Heinze et al. ( 2010) conducted AI learning activities without assessing learning outcomes. Shamir and Levin ( 2022) designed a questionnaire based on "constructionist validated robotics learning" for machine learning construction (the questionnaire included statements such as " I can make a ML system", " I can propose ideas for using ML to solve problems."). Dai et al. ( 2023) used multiple choice questions (e.g., " Which of the following devices or systems is an intelligent agent?") to evaluate the AI knowledge of primary school students according to Bloom's Taxonomy.

In secondary schools, surveys are used to measure students' information knowledge acquisition (Priya et al., 2022), perceived abilities (Chiu et al., 2021; Ng & Chu, 2021) and futuristic thinking, engagement, interactivity, and interdisciplinary thinking skills (Sakulkueakulsuk et al., 2018). For example, in Priya et al. ( 2022), surveys were used in the first phase of their study to test the knowledge gained by students in three AI areas, namely, supervised learning (e.g., " What is the underlying idea behind supervised learning?"), gradient descent (e.g., " In gradient descent how do we reach optimum point?"), and KNN classifications (e.g., " Using underlying principle of KNN classification classify a fruit which is surrounded by 2 apples and 1 mango in its nearest neighbors."). In the second phase of the study, surveys were used to evaluate students' satisfaction with the design of the game "ML Quest", which introduced machine learning concepts based on the quality factors of the technological acceptance model (e.g., "Visualizations displayed by ML-Game are relevant to the concept taught at each level").

Artifact-based/performance-based assessments are embedded in a large number of studies to evaluate learning outcomes. Through artifacts (e.g., Popbots), Williams et al., 2019a, 2019b) evaluated kindergarteners' knowledge and understanding of supervised machine learning. Ho et al. ( 2019) used a performance-based assessment to assess primary students' understanding of optimal data training and its AI applications. The artifact analysis of Shamir and Levin ( 2021) involved the construction of a rule-based AI system, which included designing, understanding, and creating the AI neural network agent. Dai et al. ( 2023) used a drawing assessment to evaluate primary school students' understanding of AI and its impact on their cognitive development using prompt questions (e.g., " What AI can do? What would you like to use AI for?") to stimulate their thinking.

Moreover, Yoder et al. ( 2020) focused on secondary school students' block-based programming artifacts to examine their knowledge of AI search algorithms and breadth-first search (BFS), as well as their understanding of the possibility of gender bias when using AI screening tools in recruitment. In Martins et al. ( 2023), machine learning model artifacts created by students were used as evidence to demonstrate their learning outcomes. The performance-based assessment was used to evaluate students' ability to correctly label the recycling trash images in the classification process.

Discussion and conclusion

In addition, educational tools and applications are used not only to contribute new ways of knowing and doing but also to embed learning tools at the center of the AI literacy activities and programs instead of playing a supporting role in the primary purpose of education. This is expanding to serve the human need for education. The use of multiple educational learning tools and pedagogical strategies may be influenced by various factors in the teaching process, including students' gender, background knowledge, and educational setting, all of which may affect their learning styles and motivation to learn AI. These factors and issues can be explored in future studies.

Research implications

From this study, the use of intelligent agents is recommended, including Teachable Machines, Machine Learning for Kids, and Learning for ML. Kindergarten students can benefit from learning tools such as PopBots, while software devices such as Scratch and Python can be introduced to demystify core AI principles to primary school students and create AI-driven solutions and models for secondary school students. Although hardware such as robotics and physical artifacts are generally effective, they may be costly for scalability.

This review reveals that constructivism, constructionism, and computational thinking are instrumental in addressing AI literacy education. Unfortunately, little research has adopted theoretical frameworks or conceptual models of reference for AI curricula, educational activities, or the design of AI learning tools and applications. To guide teaching, learning and effectiveness in using AI learning tools within AI literacy education, AI literacy learning theoretical frameworks are needed to guide the teaching instruction of kindergarten, primary and secondary school students. Usability, AI ethics, and transparency must be addressed in tool design to ensure that issues pertaining to data privacy and security will not arise. Moreover, there is currently insufficient theory-based, rigorous research on the effectiveness of AI educational tools to meet the diverse learning needs of students. Children may be invited to codesign with application designers. Thus, researchers may conduct theory-based and outcome-oriented quantitative and qualitative research on AI educational tools, which may be significantly beneficial to students.

More evaluation and documented analysis regarding the effectiveness of learning tools should be conducted to inform stakeholders of the existing trends in the field, pedagogical strategies, and instructional methods for teacher professional development.

More research, analysis, and evidence are needed to determine the effectiveness of AI learning tools before they are scaled up based on a risk-benefit analysis. Researchers should also clearly define the educational settings in which specific AI learning tools are appropriate to support the effective delivery of AI content in the classroom.

Recommendations

For educators

For teachers

Children learn best at a certain stage of cognitive development (Ghazi & Ullah, 2015). It is recommended that the content of instruction is consistent with students' cognitive developmental level, as it influences their readiness and ability to learn (Piaget, 2000). As a result, the technical and content depth of the educational learning tools should align with students' age and the teaching objectives, and teachers should understand students' cognitive development to plan age-appropriate activities with suitable learning tools. More collaboration among teachers with various pedagogical experiences across various educational levels may lead to more innovative and efficient teaching processes.

For researchers

Researchers should report evidence of the reliability. and validity of their findings where applicable since such data are crucial to evaluating the quality of their recommended learning tools or pedagogies. This can also aid other academics in updating their research on existing and developing pedagogical strategies. Researchers may consider designing and developing a standardized AI assessment mechanism that can be used across different grade levels to compare students' AI literacy. This approach permits the standardization of assessment criteria and instructional feedback and thus better supports the wider application of AI teaching in K-12 classrooms.

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Acknowledgements

The support and guidance provided by Professor Rupert Wegerif, Dr Annouchka Bayley, Dr Eleanor Dare and the University of Cambridge during this ongoing academic pursuit have been instrumental and greatly appreciated.

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1. Faculty of Education, University of Cambridge, 184 Hills Road, Cambridge, CB2 8PQ, UK

Iris Heung Yue Yim

2. Faculty of Education, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China

Jiahong Su

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1. Iris Heung Yue Yim

2. Jiahong Su

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Correspondence to Iris Heung Yue Yim.

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Appendix 1: Overview of the selected articles

Appendix 1: Overview of the selected articles

Author

Research methods

Learning tools

Pedagogies

Country

Education Level

Lin et al. ( 2021)

Quantitative

AI curriculum textbook (Qin et al., 2019) issued by the local education authority, and a device similar to Micro:bit

Direct instruction, followed by hands-on activity

China

Upper primary school

Melsión et al. ( 2021)

Mixed

Grad-CAM explainability technique in an image captioning system( https://biaix.now.sh); Google Teachable Machine

Project-based learning; experiential learning; human-computer interaction

Sweden

Upper primary school

Voulgari et al. ( 2021)

Mixed

Artbot (an educational game design employed on the Learn ML website http://learnml.eu/g ames.php)

Game-based learning

Greece

Primary and secondary school

Estevez et al. ( 2019)

Mixed

Scratch

Experiential learning:

Spain

Upper secondary school

Project-based learning

Priya et al. ( 2022)

Quantitative

Machine Learning Quest

Game-based learning;

/

Secondary school

Project-based learning

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Yim, I.H.Y., Su, J. Artificial intelligence (AI) learning tools in K-12 education: A scoping review. J. Comput. Educ. 12, 93-131 (2025). https://doi.org/10.1007/s40692-023-00304-9
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# data[].attributes.attachment_link.title
Artificial intelligence (AI) learning tools in K-12 education: A scoping review - Journal of Computers in Education

# data[].attributes.attachment_link.description
Artificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students' learning outcomes.