Guest Post: What’s the Best AI Platform for Adaptive Teaching? A Comparative Guide
This week, we’re pleased to share a guest post from our good friends at Redmenta. Redmenta is the only AI assistant for every step of the teaching process. They help teachers co-create interactive learning content, evaluate student work, and adapt activities based on performance data — making personalised learning a practical reality in every classroom.
Let’s get into it…
One class, one grade, one curriculum, but…different students. While some students can finish a task in minutes, others are still struggling to understand instructions. Sounds familiar? Every teacher must have experienced this. Struggling students may work on too difficult tasks, while high-ability students may practice skills they have already mastered (Smale-Jacobse et al., 2019). Finding a pace that keeps everyone on track and engaged is one of the biggest challenges in teaching. For instance, a qualitative study revealed that teachers, despite being aware of various strategies to address mixed-ability classrooms, often feel incapable of implementing them consistently and effectively (Mirani and Chunawala, 2015). Similarly, Gaitas and Martins (2016) found that primary school teachers perceive significant difficulties in implementing differentiated instructional strategies, reporting lack of planning time, inadequate resources, and insufficient training as primary barriers. So, how can teachers make sure that all students succeed regardless of their ability? Here’s when AI-powered adaptive teaching comes into play.
What is Adaptive Teaching?
Adaptive teaching is a method where instruction is customised based on each student's learning pace, strengths, and areas for improvement. It refers to adaptations made to teaching to ensure it provides all learners with the opportunity to meet expectations (Hawthorne, 2023). Unlike the traditional model, which often relies on a fixed curriculum and standardised assessments, adaptive teaching uses real-time data and feedback to adjust lessons dynamically. This allows teachers to modify the difficulty of tasks, provide additional support, or introduce new learning materials, ensuring that all students remain engaged and challenged. Compared to traditional one-size-fits-all approaches, adaptive teaching radically changes the mode of teaching, as it puts students' needs in focus (Sibley et al., 2024).
Note: Formative assessment plays an important role in adaptive teaching, as it provides evidence of students’ progress and identifies their strengths and weaknesses. This helps teachers to customise their instruction to meet individual student needs.
Why Adaptive Teaching Matters
Adaptive teaching revolutionises the learning experience by catering to each student’s unique needs and strengths. These are the key benefits that demonstrate how this approach promotes fairness, engagement, achievement, and teacher confidence.
Promoting equity and inclusivity
This is the most significant advantage of adaptive teaching. By adapting content, materials, and approaches, it ensures that every learner—regardless of background or ability—receives the support they need to succeed. Research shows that this personalised approach can effectively address students’ heterogeneity in the classroom (Sibley et al., 2024).
Narrowing the achievement gap
In a traditional classroom, students with diverse learning abilities are often forced to follow the same pace, leaving some students behind while others feel unchallenged. In contrast, adaptive teaching ensures that every student has a task that suits their level and abilities, fostering a sense of progress and accomplishment.
Fostering student engagement
When students are presented with content that is relevant to their current level of understanding and interests, they are more likely to stay motivated and invested in their learning. Adaptive teaching also promotes a growth mindset and fosters learner autonomy, as students are encouraged to take ownership of their learning and view challenges as opportunities for growth.
Facilitating effective lesson planning
Adaptive teaching helps teachers understand students’ prior knowledge and plan lessons accordingly. Detailed and informed planning prepares teachers for potential challenges and barriers, which can boost their confidence during lessons. This not only gives teachers more control over lesson outcomes but also creates space for them to develop and apply adaptive teaching strategies in real time.
How does AI assist teachers in adaptive teaching?
One of the key advantages of artificial intelligence in education is its capability to support personalised and adaptive learning. This personalisation can increase student engagement and improve learning outcomes. AI systems can deliver immediate feedback and generate learning tasks that are aligned with students’ individual abilities and performance levels (Mao et al., 2024). These features enable educators to offer more tailored learning experiences, addressing the unique needs, goals, and preferences of each student. Furthermore, integrating GenAI tools that offer personalised feedback and adaptive learning paths can enhance students’ awareness of their learning progress and outcomes, which will equip them with lifelong learning skills. This can disrupt a traditional one-size-fits-all approach in teaching and offer innovative ways to evaluate students’ learning based on their individual needs.
Adjust Instructions
The key to effective learning is understanding instructions. While some students grasp them immediately, others may fall behind. There are various strategies to overcome this challenge, including repetition and peer support. However, there is a much more feasible and efficient way to ensure students’ understanding by using AI tools. For instance, these tools can simplify the language of instructions according to the students’ level, break them down into small steps, or translate them into their native language.
Provide Tailored Explanations
Traditional classroom instruction often relies on one-size-fits-all explanations. However, not all students learn in the same way. Adaptive AI technologies can offer personalised support by generating alternative explanations based on individual learning preferences and progress. In a language class, for example, they might provide customised vocabulary lists, grammar tips, or real-life usage examples tailored to a student’s level and interests.
Facilitate Assessments and Feedback
Keeping track of each student’s learning progress can be time-consuming and challenging. Adaptive AI teaching platforms simplify this process by collecting and analysing data on student performance. These tools identify students’ strengths and areas for improvement, enabling teachers to provide timely, focused feedback (Simon & Zeng, 2024). Automated quizzes, interactive tasks, and student performance analysis help teachers make informed decisions and adjust their teaching strategies accordingly. In addition, AI tools can provide personalised recommendations in their feedback. For instance, certain automated feedback systems not only determine the most appropriate individual answers but also recommend additional materials and challenges. Despite various benefits of AI-generated feedback, it may lack the nuanced understanding of individual learners’ psychology, needs, and the socio-cultural context within which learning occurs (Jacobsen & Weber, 2025). However, there are AI tools which can address this issue by allowing users to customise the style and tone of AI-generated feedback.
Create Personalised Materials and Activities
Students often have different prior knowledge, learning pace, and preferred learning styles. AI allows teachers to design and deliver a variety of resources that cater to this diversity. Whether it's creating extra practice exercises for students who need more support or challenging tasks for those ready to move ahead, adaptive tools make it easy to customise materials. This ensures that all students remain motivated and learn at a pace that suits them best. Additionally, teachers can organise students into groups based on shared characteristics such as proficiency level or interests and adapt materials accordingly.
Adaptive learning platforms
As adaptive teaching is on the rise, the tech industry has responded by creating a wide range of adaptive teaching/learning platforms. These technologies enable real-time personalisation, responsive feedback, and intelligent scaffolding that aligns with individual learners' needs. While they all focus on enhancing adaptive teaching, they differ in how they personalise content, the level of educator control, and the kind of support they offer to learners. Here is an overview of different platforms.
Knewton (Alta)
Knewton is used primarily in higher education for STEM subjects. It combines practice with personalised learning that offers detailed answer explanations, integrated just-in-time instruction, and remediation of prerequisite skill gaps, all based on student performance.
Main features:
Real-time personalisation based on student performance.
Continuous formative assessments with feedback.
Instructor dashboards with learning analytics.
Integrates with major LMS platforms.
Smart Sparrow
Smart Sparrow is an adaptive learning platform widely used in higher education and medical education. It allows educators to build adaptive pathways that respond to learner behaviour, offering scaffolded feedback and targeted instruction.
Main Features:
Customizable learning pathways and rich interactive content.
Instructor control over adaptive rules.
Detailed learner analytics and insights.
Scenario-based and simulation-friendly design.
Adaptemy
Adaptemy is used in secondary and higher education, particularly for mathematics and science. It delivers personalised learning paths powered by AI-driven insights, adapting content in real time based on student responses, with immediate feedback and detailed analytics for teachers.
Main Features:
Real-time personalisation of content based on student responses and behaviour.
Intelligent feedback and scaffolding to guide learners through challenges.
Detailed analytics for teachers, including performance trends and concept mastery.
Curriculum alignment for different regions and educational standards.
Redmenta
Redmenta* is a flexible digital assessment tool used at different stages of education. It supports both in-class and remote learning, offering teachers full control over task design and adaptive pathways. The platform is effective for formative assessment, revision, and personalised practice, and is especially useful for language learning, exam preparation, and flipped classroom models.
Main Features:
Customizable worksheet creation with varied question types (e.g. open-ended, multiple choice, matching).
Conditional logic to personalise learning paths based on student answers.
Built-in scoring system and instant or delayed feedback options.
Collaborative task sharing and template use among educators.
| Category | Knewton (Alta) | Adaptemy | Smart Sparrow | Redmenta |
|---|---|---|---|---|
| Personalisation Method | Algorithm-driven adaptation based on mastery and responses. | AI-powered concept-mapping and real-time diagnostics. | Rule-based pathways created by educators with branching logic. | Rule-based personalisation using conditional paths and scoring logic. |
| Instructional Flexibility | Limited – mostly prebuilt, structured content. | Moderate – adapts language and step complexity. | High – fully customisable instructional flow. |
High – teachers build tasks with varied question types and adaptive flow. |
| Feedback & Support | Automated, standard feedback tied to mastery goals. | Personalised hints and targeted support based on misconceptions. | Dynamic feedback based on learner decisions and paths. | Feedback is automated or customised by teachers. |
| Educator Control & Customisation | Low – minimal teacher control over content or logic. | Moderate – assign and adjust, but core logic is AI-driven. | High – educators create everything from flow to feedback. | High – teacher-driven design of adaptive tasks, rules, and content. |
Challenges and barriers in adaptive teaching
Although adaptive teaching offers numerous benefits, certain limitations must be taken into account. When teachers tailor content and tasks to match students’ proficiency levels, there is a risk that their learning experience may lack sufficient challenge. This can limit opportunities for cognitive growth and hinder the development of higher-order thinking skills. Therefore, despite considering the needs of all learners, teachers should not remove the challenge entirely (Hawthorne, 2023). It needs to be present at all levels. Another challenge might be teachers’ lack of knowledge and skills to utilise adaptive tools effectively. To boost teachers’ confidence and effective use of technology, training should go beyond basic functions and focus on its practical application in teaching (Simon & Zeng, 2024). It should also foster a positive attitude toward adaptive technology before classroom implementation. Teachers have also raised concerns about aligning adaptive teaching with curriculum standards (Barber, 2024; Simon & Zeng, 2024). Adaptive approaches must be in line with curriculum objectives. Additionally, high-stakes standardised assessments require all students to meet the same benchmarks. Therefore, adaptive teaching should ultimately aim to close the achievement gap and ensure that every student reaches the expected level.
Final note
In modern education, there is a growing need to shift from the traditional one-size-fits-all approach to personalised learning, making it essential for teachers to integrate adaptive technology in their classroom. AI tools offer various features that facilitate adaptive teaching, including the ability to adjust instructions and explanations and generate personalised materials, assessments, and feedback. However, it is worth noting that adaptive teaching must balance personalised support with sufficient challenge to promote cognitive growth and higher-order thinking. For effective implementation, teachers need to ensure alignment with curriculum standards and develop strategies that help all students meet common learning outcomes.
*Editor's note: Redmenta (https://redmenta.com/) was honoured with the Best Innovation award at last year's BETT Awards in London. You can learn more about their achievement here: https://bettawards.com/winner/bett-innovation-award-2/
References
Barber, I. (2024). Adaptive Teaching: Understanding the Barriers and Enablers. Real Training. https://realtraining.co.uk/2024/10/adaptive-teaching-understanding-the-barriers-and-enablers
Gaitas, S., & Alves Martins, M. (2016). Teacher perceived difficulty in implementing differentiated instructional strategies in primary school. International Journal of Inclusive Education, 21(5), 544–556. https://doi.org/10.1080/13603116.2016.1223180
Hawthorne, H. (2023). What is Adaptive Teaching? The Hub | High Speed Training. https://www.highspeedtraining.co.uk/hub/what-is-adaptive-teaching/
Jacobsen, L. J., & Weber, K. E. (2025). The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback. AI, 6(2), 35. https://doi.org/10.3390/ai602003
Mao, J., Chen, B. & Liu, J.C. (2024). Generative Artificial Intelligence in Education and Its Implications for Assessment. TechTrends 68, 58–66. https://doi.org/10.1007/s11528-023-00911-4
Mirani, S. & Chunawala, S. (2015). Teachers' Perceptions of Dealing with Mixed Ability Classrooms. International Conference to Review Research on Science, Technology and Mathematics Education. https://www.researchgate.net/publication/292513387_Teachers'_Perceptions_of_Dealing_with_Mixed_Ability_Classrooms
Sibley, L., Lachner, A., Plicht, C., Fabian, A., Backfisch, I., Scheiter, K., & Bohl, T. (2024). Feasibility of Adaptive Teaching with Technology: Which Implementation Conditions Matter? Computers & Education, 219, 105108–105108. https://doi.org/10.1016/j.compedu.2024.105108
Simon, P. D., & Zeng, L. M. (2024). Behind the Scenes of Adaptive Learning: A Scoping Review of Teachers’ Perspectives on the Use of Adaptive Learning Technologies. Education Sciences, 14(12), 1413. https://doi.org/10.3390/educsci14121413
Smale-Jacobse, A. E., Meijer, A., Helms-Lorenz, M., & Maulana, R. (2019). Differentiated instruction in secondary education: A systematic review of research evidence. Frontiers in Psychology, 10(2366). https://doi.org/10.3389/fpsyg.2019.02366