Artem VasinTools and software can enhance collaboration between teachers and students, personalize learning, and reach remote learners who previously lacked access to quality education. Meanwhile, data analytics enables educators to track student performance, identify areas for improvement, and implement the most effective instructional strategies.
In this article, we explore key trends in educational technology that influence how knowledge is delivered and experienced – from game-based learning and VR-powered simulations to data-driven education and the assessment of open-ended responses.
Table of Contents
Artificial intelligence
When we consider the current and potential applications of artificial intelligence (AI) in education, it’s impossible to list them all, but we’ll take a look at a few.
AI algorithms, combined with predictive analytics, can analyze user behavior in educational systems to tailor content, provide hints, and dynamically adjust the difficulty of learning materials to motivate students to progress. A personalized approach that accommodates different needs and learning styles is a crucial step toward overcoming barriers in education.
In addition, educators and administrators can use machine learning models to analyze data from learning systems, such as class attendance, grades, and progress on practice assignments. This data offers valuable insights into student performance, allowing educators to identify patterns and adjust teaching methods to close knowledge gaps, understand complex concepts, and enhance academic success.
Artificial intelligence is also being utilized to support students with disabilities. For instance, AI-powered speech recognition modules can accurately convert spoken words into text, ensuring that students with hearing impairments receive more comprehensive information during lectures.
Automated learning platforms can leverage artificial intelligence to grade assignments and provide feedback. However, when working with real teachers, many students would likely prefer their work not be evaluated solely by machines. Instead, educators could use AI as an assistant to identify errors or, conversely, to highlight notable strengths in a student’s work – such as innovative approaches to problem-solving or exceptional passages in an essay – that might not be immediately apparent to the human eye.
Virtual reality (VR) and augmented reality (AR)
VR-powered simulations enable students to test scenarios in a computer-generated environment that replicates the physical world. With VR, students can safely experiment with chemical reactions, explore the laws of physics, or practice surgical procedures before applying the knowledge in real-life situations. Another application of VR is virtual field trips, allowing students to experience historical events, visit art museums and exhibitions, study animal behavior by exploring wildlife, or even witness a volcanic eruption. Many of these simulations would be difficult or impossible to recreate in a traditional classroom or at home.
AR, which overlays virtual objects onto the real environment, also enhances the learning process. For example, students can scan printed materials with their smartphones to access related 3D models or videos, adding an interactive dimension to their studies.
With 65 percent of the population being visual learners and the human brain processing images and videos much faster than text, VR and AR experiences make learning more engaging and memorable. These immersive technologies can also provide a cost-effective alternative to real-world travel for scientific research or art studies.
Gamification and game-based learning
Gamification of learning helps learners enjoy the process and feel inspired to delve into the subject matter. It incorporates principles and components of video game design, such as narrative, quests and missions, clear goals and objectives, dynamic difficulty, unlockable content, and rewards for completing tasks.
Examples of gamified learning experiences include Jeopardy-style competitions to evaluate student understanding in a pressure-free way, interactive quizzes that provide hints, and Duolingo-like platforms that gradually introduce more difficult exercises as users earn experience points.
Another concept closely related to gamification is game-based learning. Unlike lessons enhanced with game-like features, game-based learning involves using an actual video game with its unique world and characters, where learning objectives are integrated into the game’s mechanics and narrative.
One notable example of a game-based learning platform is Minecraft: Education Edition, a version of the popular world-building game designed for education. This edition includes a classroom mode with virtual chalkboards to communicate learning goals, a collection of interactive lessons and challenges in subjects such as chemistry, computer science, history, and literature, and a built-in camera mode to capture memorable achievements as evidence of student progress. Additionally, the Education Edition allows students to collaborate on projects, working together in pairs or groups.
While game-based products bring a lot of fun to learning, they don’t always guarantee improved academic outcomes. The immersive nature of these games, with their unique mechanics, can sometimes distract learners if they stray too far from the educational goals.
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Automated assessments for open-ended responses
Compared to multiple-choice questions, assessing open-ended questions is more challenging to automate, especially when they involve ill-defined tasks requiring higher-level thinking. As a result, most automated systems in education primarily use multiple-choice questions. However, this is a limited approach to assessing knowledge because the goal of teachers is to evaluate how students solve problems and whether they understand underlying principles, rather than simply memorizing facts.
Advancements in artificial intelligence have the potential to partially or fully automate the assessment of complex text- or speech-based responses. For example, natural language processing (NLP) can be used to interpret students’ essays, while speech recognition can compare foreign language students’ proficiency with that of native speakers. In addition, teachers can use computer vision technology to “photograph” a sequence of mathematical equations and determine if they follow a logical progression.
Mobile learning
Mobile learning often adopts the principles of microlearning – a method that breaks content into small, easily digestible modules focused on a single topic or concept, which can be completed in just a few minutes. The brief duration and focused content help students remember what they learn more effectively compared to traditional lessons. These short modules, created to be mastered on the go, frequently include interactive elements such as videos, motion design, quizzes, and gamified activities.
Education companies and institutions often promote this type of learning as a “healthier” alternative to endless scrolling through social media feeds or news platforms. Mobile learning apps also send notifications to encourage users to read lectures and complete assignments on a daily basis. Content quality plays a crucial role in mobile learning; regardless of how user-friendly an app is or how engaging its features are, retaining users is difficult if the content fails to deliver meaningful value.
Education platforms like Coursera and FutureLearn offer full-featured mobile apps that provide access to learning materials. These platforms also include social learning spaces, where students can ask questions, share completed coursework, and engage in asynchronous discussions often moderated by course instructors.
Mobile learning can be an affordable and effective solution in underdeveloped countries, where many people may not have access to high-performance workstations. However, modern smartphones in the low-price category are capable of handling most tasks, including online education. That said, mobile learning apps require a stable Internet connection, which can be a significant barrier for those in remote rural areas.
Finally, mobile learning is not limited to e-learning courses. This trend also includes various tools that enhance the classroom experience, such as digital flashcards, matching games, and apps for running real-time quizzes.
Video-assisted learning
Since the advent of the camera obscura, image projectors, and later the invention of motion pictures, screens have captivated artists, helping them craft compelling narratives. While video content has become a cornerstone of the visual arts, it has traditionally served only as a supplementary, presentational tool in teaching, which primarily relies on textual materials and oral lectures. In education, there is still limited research and evaluation to guide educators in using video content in their classes.
However, in a world dominated by TikTok and YouTube, which increasingly captivate minds and hearts, video-assisted learning can become a powerful tool for student engagement and communication. Video-assisted learning includes a range of resources, such as step-by-step guides for mastering skills, short explainer videos, documentaries, and language-learning videos featuring native speakers. When used in in-person lectures, it typically requires a large-screen monitor or a digital projector and a white screen.
Personalized and adaptive learning
In personalized learning, content is tailored to a particular student’s strengths, skills, needs, and interests. In other words, each student receives a learning plan based on what they already know and how they learn best.
Adaptive learning, on the other hand, is a more complex and dynamic approach. It not only uses a student’s skill level as indicated in their profile but also continuously analyzes their performance and learning pace, collects feedback, and adjusts instruction in real time.
For example, an adaptive course might provide additional materials and hints to learners who need more time to internalize knowledge or prompt them to revisit concepts they found difficult to grasp. In this approach, learners progress to a new chapter only after mastering the previous one. Conversely, if a student already knows certain information, they can skip redundant training and move on.
Personalized and adaptive learning often leverage data analytics and artificial intelligence algorithms. By analyzing data about a student’s skills and current understanding of the subject, these systems provide tailored instruction to help the student reach the next level.
Virtual assistants and chatbots
One recent example of integrating chatbots into a learning environment is Harvard University’s CS50, a world-renowned introductory course in computer science. In 2023, the course introduced a virtual teaching assistant that asks thought-provoking questions and offers suggestions to help students learn, rather than simply catching errors and fixing coding bugs.
In the near future, chatbots are expected to become increasingly integrated and sophisticated, learning from vast amounts of data and improving their ability to interact with users, thereby further enhancing the adaptive learning approach. Chatbots will also partially alleviate the workload of teachers and moderators of online courses, particularly in managing students across different time zones and with varying levels of experience.
However, as chatbots collect sensitive information to further tailor the learning experience, concerns about student privacy and ethics arise, posing significant challenges for implementation on a large scale.
In conclusion
With the rise of online education and a diverse range of learning subjects, technology is well-positioned to assist real-life teachers in maintaining a high standard of education. The principles of microlearning, adopted by mobile learning apps, can help reduce learning fatigue, while gamification and immersive reality can create engaging and memorable experiences in both classroom and remote education settings. Additionally, with machine learning and big data capable of processing massive amounts of information, teachers will gain valuable insights into their students’ performance and learning habits, enabling them to adjust teaching strategies to make education more appealing.
