The future of remote learning and its role in preparing students for future careers

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The future of remote learning still holds significant potential in preparing students for future careers by leveraging advanced technologies, fostering flexibility and accessibility, promoting lifelong learning, facilitating global collaboration, emphasizing soft skills, personalizing learning experiences, integrating industry partnerships, and ensuring ethical and responsible use of technology. A , and many institutions continue to expand their online offerings with many institutions at least offering some part of their learning online.

By embracing these opportunities and addressing challenges proactively, remote learning can play a vital role in equipping students with the knowledge, skills and mindset needed to thrive in a rapidly evolving job market. With jobs moving away from the bricks-and-mortar workplace, tomorrow's job opportunities will have fewer physical and geographical restrictions, so if you you can also learn anywhere in the world.

Here's how remote learning integrates into this evolution, by enforcing these things:

Flexibility and accessibility

Remote learning provides flexibility in scheduling and accessibility to educational resources, allowing students to balance learning with other commitments such as work or family responsibilities. This flexibility mirrors the dynamic nature of many future careers, where adaptability and time management are essential skills. It also allows people who may otherwise not be able to attend a traditional institution in person the same opportunity to learn.

Digital literacy and technological proficiency

Remote learning requires students to navigate various digital platforms, tools and resources, fostering digital literacy and technological proficiency. These skills are increasingly vital in virtually every industry, as technology continues to transform the way we work and communicate.

Self-directed learning and autonomy

Remote learning often encourages self-directed learning, where students take greater responsibility for their education, set learning goals, and manage their progress independently. This cultivates autonomy and self-motivation, qualities highly valued in the modern workplace, where employees are expected to take initiative and drive their own professional development.

Global perspective and cultural competence

Remote learning platforms facilitate collaboration and interaction among students from diverse cultural backgrounds and geographical locations. Engaging with peers from around the world fosters a global perspective, cultural competence, and cross-cultural communication skills, essential for success in an increasingly interconnected and globalized workforce.

Collaboration and teamwork skills

While remote, students often participate in group projects, discussions and virtual teamwork exercises, honing their collaboration and communication skills in digital environments. These skills are invaluable in many careers, where collaboration across distributed teams and effective virtual communication are becoming increasingly common.

Problem-solving and critical thinking

Remote learning activities often emphasize problem-solving, critical thinking and analytical skills as students engage with complex challenges and real-world scenarios. These cognitive skills are essential for navigating uncertainty and innovation in future careers, where the ability to analyze information, think critically and propose creative solutions is highly prized.

Adaptability to changing technologies and industries

Remote learning exposes students to a variety of digital tools, software and online platforms, helping them adapt to rapidly changing technologies and industries. By staying abreast of emerging trends and mastering new tools, students develop a mindset of lifelong learning and adaptability, crucial for success in careers that continually evolve.

Professional networking and career development

Remote learning platforms often provide opportunities for students to connect with industry professionals, mentors and alumni networks, expanding their professional network and gaining insights into potential career paths. Building relationships with professionals in their field of interest can open doors to internships, job opportunities and career guidance, facilitating a smoother transition from education to employment.

The future of remote learning is poised to undergo significant transformations driven by technological advancements, evolving educational paradigms and societal shifts. Here are just some key trends that may shape the future of remote learning…

Hybrid learning models

As we move forward, we're likely to see a blend of traditional classroom instruction and remote learning, known as hybrid learning. This model offers flexibility for both students and educators, allowing them to leverage the benefits of in-person interaction while also harnessing the advantages of remote technologies for convenience, accessibility and personalized learning experiences.

Virtual reality (VR) and augmented reality (AR)

VR and AR technologies are expected to play a more prominent role in remote learning, offering immersive and interactive experiences that simulate real-world environments. These technologies can transport students to historical sites, enable hands-on experiments in science labs, and facilitate collaborative problem-solving activities, enhancing engagement and understanding.

Artificial intelligence (AI) and adaptive learning

AI-powered tools can analyze vast amounts of data to personalize learning experiences, identify individual strengths and weaknesses, and offer targeted interventions. Adaptive learning platforms can adjust the pace, content and instructional methods based on each student's learning preferences and performance, optimizing learning outcomes and promoting mastery of concepts.

Microlearning and bite-sized content

With attention spans decreasing and the demand for just-in-time learning increasing, microlearning—delivering content in small, digestible chunks—is gaining popularity. Microlearning modules, delivered through mobile devices and online platforms, enable learners to access relevant information quickly, retain knowledge more effectively, and apply it immediately in real-world contexts.

Gamification and interactive content

Gamification elements such as badges, leaderboards and rewards can enhance motivation, engagement and retention in remote learning environments. Interactive content, such as simulations, quizzes and branching scenarios, not only makes learning more enjoyable but also facilitates active participation, critical thinking and skill development.

Social learning and collaboration tools

Remote learning platforms will continue to integrate social learning features such as discussion forums, group projects and peer feedback mechanisms. Collaboration tools like video conferencing, instant messaging and shared document editing enable synchronous and asynchronous communication, fostering interaction, teamwork and community building among students and educators.

Data-driven decision making

The collection and analysis of learning analytics data will enable educators and administrators to gain insights into student progress, engagement levels and learning preferences. By leveraging data-driven decision-making processes, institutions can identify areas for improvement, personalize interventions and optimize instructional strategies to enhance student success and retention rates.

Continued emphasis on equity and inclusion

As remote learning becomes more prevalent, it's crucial to address issues of access, digital literacy, and socioeconomic disparities. Efforts to bridge the digital divide, provide equitable access to technology and resources, and create inclusive learning environments for diverse populations will be essential in shaping the future of remote learning.

In summary, by leveraging the opportunities provided by remote learning, students, job seekers and career changers can develop the skills and competencies needed to thrive in a dynamic and rapidly changing job market.

Read more in our blog post 'The advantages of online distance learning'.

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  • Hands typing at a laptop with symbols

    Can computers really mark exams? Benefits of ELT automated assessments

    Por app Languages

    Automated assessment, including the use of Artificial Intelligence (AI), is one of the latest education tech solutions. It speeds up exam marking times, removes human biases, and is as accurate and at least as reliable as human examiners. As innovations go, this one is a real game-changer for teachers and students. 

    However, it has understandably been met with many questions and sometimes skepticism in the ELT community – can computers really mark speaking and writing exams accurately? 

    The answer is a resounding yes. Students from all parts of the world already take AI-graded tests.  aԻ Versanttests – for example – provide unbiased, fair and fast automated scoring for speaking and writing exams – irrespective of where the test takers live, or what their accent or gender is. 

    This article will explain the main processes involved in AI automated scoring and make the point that AI technologies are built on the foundations of consistent expert human judgments. So, let’s clear up the confusion around automated scoring and AI and look into how it can help teachers and students alike. 

    AI versus traditional automated scoring

    First of all, let’s distinguish between traditional automated scoring and AI. When we talk about automated scoring, generally, we mean scoring items that are either multiple-choice or cloze items. You may have to reorder sentences, choose from a drop-down list, insert a missing word- that sort of thing. These question types are designed to test particular skills and automated scoring ensures that they can be marked quickly and accurately every time.

    While automatically scored items like these can be used to assess receptive skills such as listening and reading comprehension, they cannot mark the productive skills of writing and speaking. Every student's response in writing and speaking items will be different, so how can computers mark them?

    This is where AI comes in. 

    We hear a lot about how AI is increasingly being used in areas where there is a need to deal with large amounts of unstructured data, effectively and 100% accurately – like in medical diagnostics, for example. In language testing, AI uses specialized computer software to grade written and oral tests. 

    How AI is used to score speaking exams

    The first step is to build an acoustic model for each language that can recognize speech and convert it into waveforms and text. While this technology used to be very unusual, most of our smartphones can do this now. 

    These acoustic models are then trained to score every single prompt or item on a test. We do this by using human expert raters to score the items first, using double marking. They score hundreds of oral responses for each item, and these ‘Standards’ are then used to train the engine. 

    Next, we validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. If this doesn’t happen for any item, we remove it, as it must match the standard set by human markers. We expect a correlation of between .95-.99. That means that tests will be marked between 95-99% exactly the same as human-marked samples. 

    This is incredibly high compared to the reliability of human-marked speaking tests. In essence, we use a group of highly expert human raters to train the AI engine, and then their standard is replicated time after time.  

    How AI is used to score writing exams

    Our AI writing scoring uses a technology called . LSA is a natural language processing technique that can analyze and score writing, based on the meaning behind words – and not just their superficial characteristics. 

    Similarly to our speech recognition acoustic models, we first establish a language-specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. 

    Once the language model has been established, we train the engine to score every written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. 

    The benchmark is always the expert human scores. If our AI system doesn’t closely match the scores given by human markers, we remove the item, as it is essential to match the standard set by human markers.

    AI’s ability to mark multiple traits 

    One of the challenges human markers face in scoring speaking and written items is assessing many traits on a single item. For example, when assessing and scoring speaking, they may need to give separate scores for content, fluency and pronunciation. 

    In written responses, markers may need to score a piece of writing for vocabulary, style and grammar. Effectively, they may need to mark every single item at least three times, maybe more. However, once we have trained the AI systems on every trait score in speaking and writing, they can then mark items on any number of traits instantaneously – and without error. 

    AI’s lack of bias

    A fundamental premise for any test is that no advantage or disadvantage should be given to any candidate. In other words, there should be no positive or negative bias. This can be very difficult to achieve in human-marked speaking and written assessments. In fact, candidates often feel they may have received a different score if someone else had heard them or read their work.

    Our AI systems eradicate the issue of bias. This is done by ensuring our speaking and writing AI systems are trained on an extensive range of human accents and writing types. 

    We don’t want perfect native-speaking accents or writing styles to train our engines. We use representative non-native samples from across the world. When we initially set up our AI systems for speaking and writing scoring, we trialed our items and trained our engines using millions of student responses. We continue to do this now as new items are developed.

    The benefits of AI automated assessment

    There is nothing wrong with hand-marking homework tests and exams. In fact, it is essential for teachers to get to know their students and provide personal feedback and advice. However, manually correcting hundreds of tests, daily or weekly, can be repetitive, time-consuming, not always reliable and takes time away from working alongside students in the classroom. The use of AI in formative and summative assessments can increase assessed practice time for students and reduce the marking load for teachers.

    Language learning takes time, lots of time to progress to high levels of proficiency. The blended use of AI can:

    • address the increasing importance of formative assessmentto drive personalized learning and diagnostic assessment feedback 

    • allow students to practice and get instant feedback inside and outside of allocated teaching time

    • address the issue of teacher workload

    • create a virtuous combination between humans and machines, taking advantage of what humans do best and what machines do best. 

    • provide fair, fast and unbiased summative assessment scores in high-stakes testing.

    We hope this article has answered a few burning questions about how AI is used to assess speaking and writing in our language tests. An interesting quote from Fei-Fei Li, Chief scientist at Google and Stanford Professor describes AI like this:

    “I often tell my students not to be misled by the name ‘artificial intelligence’ — there is nothing artificial about it; A.I. is made by humans, intended to behave [like] humans and, ultimately, to impact human lives and human society.”

    AI in formative and summative assessments will never replace the role of teachers. AI will support teachers, provide endless opportunities for students to improve, and provide a solution to slow, unreliable and often unfair high-stakes assessments.

    Examples of AI assessments in ELT

    At app, we have developed a range of assessments using AI technology.

    Versant

    The Versant tests are a great tool to help establish language proficiency benchmarks in any school, organization or business. They are specifically designed for placement tests to determine the appropriate level for the learner.

    PTE Academic

    The  is aimed at those who need to prove their level of English for a university place, a job or a visa. It uses AI to score tests and results are available within five days. 

    app English International Certificate (PEIC)

    app English International Certificate (PEIC) also uses automated assessment technology. With a two-hour test available on-demand to take at home or at school (or at a secure test center). Using a combination of advanced speech recognition and exam grading technology and the expertise of professional ELT exam markers worldwide, our patented software can measure English language ability.