Beige? AI impact on learners

Beige: Will AI compress the learning bell curve?

This is perhaps the heart of the challenge for schools in the age of AI. On the one hand, there are concerns that AI could homogenise learning outcomes, compressing the bell curve of learning into the middle. This fear arises from the possibility that less capable students might use AI tools to artificially enhance their grades, thereby circumventing the learning process and curtailing their personal growth. Similarly, high-achieving students could become complacent, outsourcing their efforts to AI to deliver ‘good enough’ work without pushing their boundaries or furthering their capabilities.

AI will Improve all learners’ Outcomes

There is an alternative, more optimistic view. This perspective suggests that AI could facilitate a positive shift in the learning curve for all students, without diminishing the spread of abilities. For those students currently underperforming, AI could offer personalised scaffolding and carefully calibrated stretch challenges, fostering authentic growth rather than just superficially enhancing their grades. The technology could provide the differentiated support they need to fully engage with the learning process and make meaningful progress.
Similarly, for high-achieving students, AI offers the potential to augment their capabilities and set ambitious stretch goals. It provides a powerful tool for them to go beyond the ‘good enough’, stimulating them to explore the edges of their potential. It is here that AI becomes a catalyst for excellence, encouraging students to delve deeper into complex concepts, hone their critical thinking, and engage with more challenging problems.

In essence, the true potential of AI lies not in creating a homogenised learning landscape, but in shifting the entire spectrum of learning outcomes in a positive direction. AI, when implemented with a deep understanding of individual student needs and potential, could help every student attain their best, irrespective of their starting point. It transforms the concept of ‘average’ by elevating everyone’s learning journey. However, this potential can only be realised if educators fully embrace AI’s capabilities and harness them in ways that promote authentic learning and growth.

Education Disruptors improve Pedagogy

Artificial Intelligence represents the latest in a line of major disruptors within the educational landscape, joining the ranks of books and computers in their transformative influence. As with its predecessors, AI is not simply enhancing the outcomes of learning, but it is catalysing a paradigm shift in pedagogical practice, echoing the profound changes triggered by books and computers.
The introduction of books in education moved the teacher from the position of an all-knowing sage delivering information, to a guide facilitating the exploration of knowledge. Computers further reinforced this shift, underscoring that education is not merely the accumulation of information, but more about the creative and critical application of knowledge, and the ability to collaborate effectively.
In the same spirit, AI pushes these boundaries even further. It goes beyond augmenting learners’ abilities and knowledge, initiating changes in how teachers teach, and fostering the adoption of superior pedagogical practices. AI promotes more reflective learning practices, encouraging students to engage in iterative processes of trial, error, and improvement. It fosters greater student autonomy, supporting personalised learning journeys tailored to individual needs.
AI also prompts a shift away from traditional, outcome-oriented education towards a problem-solving approach where learning is driven by authentic, relevant challenges. This not only bolsters creativity and productivity, but it also enhances the efficiency of the learning process. It demands a greater focus on ethics and character development, as learners navigate an increasingly complex digital world. It elevates the intrinsic value of learning, providing students with meaningful, impactful experiences that connect with their local contexts.

User Centric/ Personalised learning

AI has the capacity to revolutionise education by promoting a user-centric pedagogy, where the learner is at the heart of the education process. This is particularly evident in the concept of personalised learning, a teaching model that caters to the unique needs, skills, and interests of each student.
The evolution of finding resource material serves as a prime example of how AI enhances user-centric learning. In the era of physical libraries, students had to sift through extensive library catalogues and endless bookshelves, a task that was time-consuming and sometimes hit-or-miss depending on the library’s resources.
The advent of the internet made it possible to access a global library. This drastically increased the breadth of available resources but introduced a new challenge: information overload. The task of finding pertinent materials essentially remained the same, only now it involved trawling through an exponentially larger, often overwhelming, amount of information in search results.
With AI, this process undergoes yet another transformation. Instead of the student adapting to the tool (i.e., navigating library systems or search engines), the tool adapts to the student. AI can understand the specific needs and conceptual level of each individual learner and return or reference appropriate resource material accordingly.
For example, if a student is struggling to understand the concept of photosynthesis, an AI system can assess the student’s current understanding level, and then suggest resources ranging from simple explanatory videos to more complex scientific articles. The AI tool could also provide interactive quizzes to test comprehension or generate summaries of key points.
This transition represents a profound shift from a hopeful trawling through resources to a bespoke, user-focused process. The learning journey is now a much more efficient and personalised experience, tailored to each student’s unique learning pathway. This is the power of AI in promoting user-centric pedagogy and personalised learning.

Enhanced Student Impact and improved created work

AI can be an incredibly powerful tool in enhancing the impact of student work and improving its quality. By providing students with the resources to generate higher-level ideas, better their problem-solving skills, and create more complex projects, AI can contribute significantly to the level of student output.
AI’s role in enhancing student impact can be seen clearly in the area of scientific research. Traditionally, students had to manually review a multitude of scientific articles and extract relevant information to formulate a research hypothesis. This was an arduous and time-consuming process, prone to oversight and bias.
Today, AI research assistants can do this work much more efficiently. For instance, AI can trawl through thousands of scientific papers, extracting relevant data, recognising patterns, and suggesting potential hypotheses. The student can then focus on evaluating and building upon these ideas, leading to more robust and innovative research projects.
This use of AI transforms the research process from a laborious manual task into a dynamic, creative one. The student’s work evolves from simply reproducing existing knowledge to generating new, original ideas and creating a real impact in their field of study.

Universal Accessibility and Inclusivity

AI in education has the potential to create an inclusive learning environment that is accessible to all students, regardless of their abilities, learning styles, or geographical locations. It can remove barriers to education and ensure equal opportunities for everyone, promoting universal accessibility and inclusivity.
One prominent application of AI in fostering accessibility and inclusivity is its use in supporting students with disabilities. Traditionally, these students may have faced significant challenges in accessing education, such as physical barriers, lack of suitable teaching resources, or insufficient personal support.
However, AI can help mitigate these issues. For instance, students with visual impairments can use AI-driven tools that provide text-to-speech functionality, converting written content into audio. For students with hearing impairments, AI systems can offer real-time transcription services, converting spoken language into written text.
These AI-driven solutions ensure that learning materials are accessible to all students, regardless of their physical abilities, thus fostering an inclusive learning environment.

Richer Data Analysis and Insights

AI holds tremendous potential to enhance education through richer data analysis and insights. It can help educators gain a deeper understanding of student performance, progress, and learning patterns, enabling more informed decision-making and facilitating personalised learning experiences.
Consider the task of grading assignments in a large classroom setting. Traditionally, this task would require a significant amount of time and effort from educators, and their ability to provide detailed feedback might be limited by these constraints. Additionally, extracting comprehensive insights from students’ work would be challenging.
However, AI can dramatically change this scenario. AI-powered tools can assist in grading assignments, saving educators’ time and providing more immediate feedback to students. Beyond just assigning grades, these tools can analyse patterns in students’ responses, identifying common areas of struggle or confusion.
For instance, an AI system might detect that a significant number of students are making similar mistakes in solving a particular math problem. This information could alert the educator to a potential gap in understanding, enabling them to address this issue in future lessons.

Improved reflective practice

Reflective practice is an integral part of the learning process, enabling learners to review and consolidate their understanding, identify areas of improvement, and make connections between different concepts. AI can be an exceptional tool in promoting such practice, enabling learners to critically examine their own work, and evaluate the suggestions provided by the AI. It can facilitate an engaging dialogue between the student and the AI, guiding them towards a more profound understanding.
An example of AI promoting reflective practice can be found in the use of AI-driven writing assistance. A student who has completed a writing task could use the AI tool to review and enhance their work. The tool could analyse the student’s writing, highlighting strong areas and suggesting improvements where needed. At the same time, the tool could provide alternative formulations or structure suggestions generated by AI. The student then enters into a reflective process where they evaluate not only their own work but also the AI suggestions, thinking critically about the strengths and weaknesses of both. This double-layered reflection can lead to a deeper understanding and improvement of their writing skills.

Elevated Student Autonomy

Artificial Intelligence stands at the forefront of a pedagogical shift, emphasising the cultivation of student autonomy and the centring of intrinsic learning values. By leveraging AI, the learning process can be transformed from a one-size-fits-all model to a deeply personal journey. AI affords the opportunity for learning to be not just about achieving grades, but about engaging with content that is inherently meaningful to each student. With this focus, AI moves students from passive recipients to active participants in their education, empowering them to shape their learning experiences according to their pace, interests, and needs. The limitations of traditional classroom structures, defined by capacity and staff-student ratios, are circumvented with AI’s ability to provide individualised learning experiences for every student. This revolution gives rise to an enhanced learning environment that promotes curiosity, self-reliance, and a robust sense of ownership over one’s educational journey.
One example of AI fostering student autonomy is the use of AI-driven learning platforms that cater content and learning paths to each individual. These systems not only adapt the pace and difficulty level of content but can tune the learning pedagogy to match the student’s needs and preferences. Students aren’t just consumers of information, but active participants in shaping their learning experiences.
For instance, a student passionate about visual arts might receive resources and tasks that weave art history and technique into the study of different subjects. This way, not only is learning personalised, it’s also intrinsically valuable, keeping students engaged and invested in their education.

Problem solving, not minimum standards

Artificial Intelligence brings forth a transformative paradigm in education, moving away from traditional models of “teaching to the test” and rigid adherence to minimum outcome standards. In an AI-enabled learning environment, the focus shifts towards equipping learners to be dynamic problem-solvers and critical thinkers. With AI capable of assisting learners in achieving traditional minimum standards, these benchmarks alone cease to be the ultimate goal of education. The real-world doesn’t require mastery of a set curriculum, but instead, values the ability to solve complex problems and fill gaps in understanding and knowledge. Therefore, an AI-infused education system can liberate learners from the confines of a one-size-fits-all curriculum, enabling them to focus on understanding concepts deeply and applying them in innovative ways to solve real-world problems. We need to shift assessment away from final product to process.
Consider an AI-powered project-based learning platform that presents students with real-world problems tailored to their learning levels and interests. For instance, a student interested in environmental issues could be tasked with developing a plan to reduce the carbon footprint of their school. The AI system provides a framework to guide them through the process, from researching and understanding the problem, brainstorming solutions, developing a detailed plan, to reflecting on the results. The AI could then evaluate their work not only on the quality of the final product but on the process they used to get there, including their research skills, creativity, and problem-solving strategies.