Why Competitions Work

Competitions tap into students’ natural drive to succeed, turning learning into an experience rather than an obligation. They create an environment where students are not just working towards grades but actively participating in a journey of discovery and growth.

Lana Svonavec, HOD at Science Tintern Grammar School and one of the educators featured in the webinar, emphasised the impact of competitions:

“Competitions offer students a fun, dynamic way to engage with content. It’s not just about winning – it’s about seeing how far they can push themselves and discovering their own potential.”

When students see measurable progress in a competitive setting, it reinforces their confidence and willingness to engage more deeply with the subject matter.

Encouraging Motivation and Resilience

A key benefit of competitions is their ability to motivate students to take ownership of their learning. When students are given a challenge, whether an individual or team-based competition, they often rise to the occasion, setting personal goals and striving to improve.

Kiraly Payne, a Language Executive Teacher at Amaroo School, ACT, highlighted how competition enhances perseverance: “When students feel seen and supported, they’re more willing to take risks, make mistakes, and engage fully in the learning process.” 

This growth mindset not only helps students academically but also builds resilience, teaching them to embrace challenges as opportunities rather than obstacles.

The Role of Digital Platforms in Competitions

Technology has revolutionised the way academic competitions are run, making them more accessible, engaging, and rewarding. With platforms like EP, students can participate in global challenges that give them a sense of connection beyond their classroom walls.

Lana Svonavec shared her experience with EP competitions: “Competitions like the EP Languages Championship and the EP Science Championship allow students to see tangible rewards for their efforts, helping them feel accomplished and motivated to keep learning.” 

These competitions provide instant feedback, interactive content, and leaderboards, making the experience immersive and rewarding.

Building a Collaborative Learning Culture

While competition is often associated with individual achievement, it can also foster collaboration and teamwork. Many EP competitions encourage students to work together towards common goals, creating a shared sense of purpose and camaraderie.

Through collaboration, students develop communication skills, critical thinking and peer learning, all essential skills for future success. Whether in language challenges, maths quizzes, or science problem-solving tasks, competitions offer an engaging way to reinforce teamwork and shared learning.

A Tool for Every Student

Perhaps one of the most significant advantages of academic competitions is their inclusivity. They cater to a wide range of learners – whether high achievers looking for a challenge or students who may need a new way to engage with content.

Kiraly Payne summed it up perfectly: “Students aren’t just sitting back and absorbing information—they’re interacting with it. They’re testing themselves, learning through doing, and staying much more engaged.”

Competitions provide multiple entry points for engagement, ensuring that all students, regardless of their starting point, can find success and enjoyment in learning.

Unlocking Potential Through Competition

The insights shared by these educators reinforce a fundamental truth: competition, when structured thoughtfully, is a powerful catalyst for student engagement and learning. It brings energy into the classroom, drives students to challenge themselves, and fosters a culture of perseverance and collaboration.

By incorporating competitions into the learning experience – whether through digital platforms, classroom challenges, or global events – educators can inspire students to embrace learning with enthusiasm and confidence. As students push their boundaries and celebrate their progress, they develop not just knowledge but a lifelong passion for discovery.

Want to bring the power of competitions into your classroom? 

Take part in our EP World Series Competitions, free for all students with an EP account across Australia, New Zealand and select International Schools.

Want to add a splash of colour to your classroom walls? Download our free Languages Championships posters here!

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As AI becomes more prevalent in education, 2025 presents an opportunity to define it as the “Year of Efficacy in AI and EdTech.”

In Education Perfect’s (EP) recent Independent and Catholic School Roundtable discussions on assessing the impact of AI tools in secondary education, school leaders shared where they’re at in their AI journey: what’s working, what’s worrying, and what’s next for them.

Here are the key takeaways from the discussion:

1. AI Implementation & School Policies

The journey to AI adoption in schools is varied, with many institutions still in the early stages of exploration. A major theme that emerged was the need for clear AI policies and frameworks, as the speed of implementation is often dictated by system policies, and many are waiting on clearer guidelines.

One message rang loud: a thoughtful, school-wide AI framework is crucial for any school. It allows educators to confidently engage with AI tools while ensuring that privacy, security and ethical considerations are prioritised. As AI tools become more integrated into classrooms, schools are looking for ways to balance innovation with compliance, ensuring that AI usage aligns with all educational objectives.

2. Data Security & AI Tool Evaluation

As schools are increasingly selective about the AI solutions they adopt, IT teams are conducting rigorous quality checks to ensure tools meet privacy and safety standards. These evaluations are not only guided by system policies but also by a deep commitment to safeguarding staff and student data.

Schools are aware of the risks AI tools may present to data privacy, and many are still in the process of assessing which tools can be trusted. Before fully integrating AI into their systems, educators and administrators require concrete data on how AI will impact learning outcomes. At EP, we take these standards seriously, ensuring our AI tools meet the highest data security expectations.

3. AI in Teaching & Learning

One of the most promising uses of AI in education is its ability to alleviate the administrative burden on teachers. AI tools are being used to streamline lesson planning, report writing and feedback generation, allowing educators to spend more time engaging with students. Additionally, AI provides instant formative feedback on student tasks, enabling timely interventions and learning adjustments.

AI is also being explored for personalised learning, with some tools leveraging AI-driven learner profiles to customise assignments based on student capabilities. This approach has proven beneficial for students with lower literacy levels, as AI can adapt content to match their needs, making learning more accessible.

4. AI & Academic Integrity

As AI tools become more integrated into classrooms, concerns about academic integrity are growing. False positives from AI-detection tools, and the risk of over-reliance on AI for assignments, have schools looking for balance. Teachers are responding by embedding transparency, encouraging critical thinking, and developing students’ ability to evaluate AI-generated content.

Studies suggest that students performing at a mid-range academic level may benefit the most from AI interventions, while high-achieving students may see a plateau. More research is needed to assess the long-term effects of AI on students at all academic levels.

5. The Quest for Evidence: A Desert of Data

While AI’s administrative benefits are clear, schools are still asking: Is it actually improving learning outcomes? As EP’s co-founder, Shane Smith put it, “We’re in an ocean of possibility but a desert of evidence.” Schools want to see hard data, and that’s where EP is stepping up. We’re running a formal AI efficacy trial in Term 2 of 2025 to measure the impact of AI on student learning and teacher wellbeing.

6. AI’s Role in Human Relationships

The school leaders unanimously agreed: AI should not replace teachers but should support them in their efforts to create meaningful connections with students. While AI can streamline certain tasks, the core focus of teaching remains human connection and fostering critical thinking skills.

AI tools should enhance the teacher-student relationship, not diminish it. When used thoughtfully, AI can empower educators by providing them with data-driven insights and freeing up time to engage more deeply with their students.

Final Thoughts

AI is an exciting tool for the classroom, but schools are still in the process of determining how to integrate it effectively. With privacy, security, and ethical use top of mind, the path forward demands a thoughtful, research-driven approach.

At EP, we believe AI should empower educators, not replace them. That’s why we’re focused on ensuring AI tools enhance teaching and learning while keeping human connection at education’s heart. Through ongoing research trials, school partnerships, and professional learning, we’re helping shape a future where AI is not just secure, but truly impactful.

Interested in measuring the impact of AI feedback in your school? Trial our tool for free today!

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To gain further insights into how it can support student growth and drive engagement, we recently completed a detailed 10-week study of more than 19,500 Australian and New Zealand intermediate and secondary school students and their 210,000 answers to measure the before-and-after impact of using our AI-powered feedback tool and learning loops¹.

We analysed 100,000 answers before the learning loop was switched on, and 104,000 answers after it was switched on to allow for comparisons to be made².

However, before we get into the results, what exactly is a learning loop, and why is it becoming crucial for effective education in the 21st century?

Understanding the Learning Loop

At its core, the learning loop is an iterative process that allows students to receive feedback, apply it to improve their work, and then receive further feedback on their improvements. This cyclical approach contrasts the traditional model of feedback cycles, where students receive a delayed evaluation of their efforts, without the opportunity to learn from their mistakes and try again while in the flow of learning.

In the learning loop, AI analyses the student’s initial response and provides specific, actionable feedback, highlighting areas for improvement. This guides the student towards a better understanding of the concepts, freeing teachers to focus on areas they are most needed.

With this feedback, students can revise and refine their work, applying what they’ve learned. When the student submits their updated response, the system analyses it again, offering further guidance and suggestions. This iterative process continues, with the student progressing through multiple rounds of feedback and improvement. AI is crucial in this practice as implementing learning loops without it increases the workload on teachers to unsustainable levels.

Without that immediate feedback and the opportunity to improve, most students don’t make second or more attempts at improving, and just stop at their first attempt. The first key finding from our study backed this up.

Finding #1 – Students engage more effectively with the learning loop activated.

• Our activated learning loop is designed to encourage students whose responses were assessed as low quality³ to have a second (and third if needed) attempt to improve.
• Our data shows that compared to self-directed improvement, this drives significantly more attempts for students submitting low-quality responses.
• Of students submitting a 1-star response as their first attempt, 83% go on to have a second attempt with the learning loop on, vs. 7% with it off.
• Of students submitting a 2-star response as their first attempt, 92% go on to have a second attempt with the learning loop on, vs. 17% with it off.
• Note: Only 1-star and 2-star responses trigger the learning loop.

The results above show an incredible increase in the number of students with low-quality initial attempts. This trend was repeated when looking at second attempts that then progressed to third attempts, as shown below.

The Power of Iterative Learning

The learning loop approach is powerful because it aligns with how humans naturally learn and grow. By allowing students to actively engage with the material, receive personalised guidance, and demonstrate their learning progress over time, we provide an opportunity for them to develop a deeper, more lasting understanding.

Traditional feedback models often fall short in this regard. A single lesson or assignment may measure a student’s knowledge at a particular moment, but it relies on a student’s internal motivation to develop the ongoing learning and skill development that is essential for long-term success. In contrast, the Education Perfect learning loop keeps students actively involved in the learning process, empowering them to take ownership of their growth and development.

Moreover, learning loops can provide valuable insights to support educators’ planning. By analysing the iterative feedback and improvement process, teachers can gain a more nuanced understanding of each student’s strengths, weaknesses, and learning patterns. This information can then be used to tailor instruction, provide targeted support, and ensure every student has the resources they need to thrive. Over time, with the learning loop in place, we should see improvements on average in the quality of student responses, which can be seen in the second finding from our study.

Finding #2 – The enforced learning loop results in higher star-rated student responses.

• With the learning loop in place, the overall average student response quality increased from 2.4 stars (out of 5) to 3.6 stars. A 47% improvement.

We also saw that enabling the learning loop significantly improves the star-rating-based quality of final responses (i.e. after all attempts have been made) across the board. The really pleasing aspect of this was seeing lower 1-star and 2-star final responses drop from 52% to just 13%, as shown below.

What can be inferred from these results?

It is important to reiterate that these metrics are based on student answer quality in an assisted scenario, rather than a direct measure of student learning. However, the results are still exciting when viewed in this context.

When we examine what is happening more closely, a pattern emerges, as shown in our third finding.

Finding #3 – The learning loop both improves student first-attempt quality and raises low-quality first-attempt responses to a minimum standard.

• Student first attempts are significantly better with the learning loop on, improving from an average of 2.37 stars to 3.05 stars.
• However, even with this higher starting point, students still improve from their first attempt to their final attempt by 0.52 stars on average with the learning loop on, versus 0.14 stars on average without the learning loop.

Our hypothesis in this situation is that students are trying harder on the first attempt as they know they’re being held accountable for their results, and their attempts won’t “fly under the radar” or get missed. We really need to see more data before we can prove this hypothesis, but the early signs are there.

As we noted earlier, the low-quality first responses improve the most with the learning loop enabled. In contrast, without the learning loop, first responses remain low quality with only minor improvement, as shown below.

An important point to note with this data is that while students with 3-star or 4-star first responses do achieve some growth and improvement, it is relatively minimal. This is not entirely unexpected as the Learning Loop lets students with a 3-star response continue without requiring them to improve or have further attempts. What would be interesting to test is whether offering students with 3-star first attempts to have another go would push the whole distribution closer to the 4-star mark. A study on this hypothesis is currently underway and early indication is looking positive.

The Role of AI

These early results are certainly exciting, but they also spotlight AI as an educational support tool. As educational technology continues to evolve, the learning loop is poised to play an increasingly crucial role in shaping the future of teaching and learning. By harnessing the power of AI, machine learning, and other advanced tools in a supporting role, educators can create dynamic, personalised learning experiences that unlock the full potential of every student.

The underlying AI model used in an educational learning loop system can significantly impact the overall effectiveness and quality of this type of learning loop experience. We delve deeper into this topic in our AI Buyer Guide article, but the key points are:

1. Accuracy and Relevance of Feedback
   The sophistication and capabilities of the AI model directly determine the accuracy and relevance of the feedback provided to students. More advanced language models, like those used by Education Perfect, can better understand the nuances of student responses and provide tailored, insightful feedback. Less capable models may struggle to identify the right improvement areas, leading to generic or misleading feedback.
2. Breadth of Question Types
   The flexibility of the AI model dictates the types of questions and prompts the system can effectively handle. Advanced models can engage students with open-ended, complex questions that require higher-order thinking. Simpler models may be limited to more basic, multiple-choice or short-response tasks, which can constrain the learning experience.
3. Iterative Improvement Tracking
   To enable a true learning loop, the AI system must be able to track and analyse a student’s progress over multiple iterations. More robust models can maintain context and memory, recognising how students’ responses evolve and provide increasingly targeted guidance. Weaker models may struggle to connect the dots, leading to a disjointed, less effective feedback loop.
4. Reliability and Consistency
   Students and teachers need to be able to trust the reliability and consistency of AI-powered feedback. Advanced models trained on larger, higher-quality datasets are less prone to making mistakes or providing contradictory guidance. Inconsistent or erratic feedback can undermine the learning loop and frustrate both students and educators.

By carefully considering the capabilities of the underlying AI model, education providers can ensure that their learning loop systems are truly effective in driving student growth and learning. The more advanced and tailored the AI, the more powerful and transformative the learning loop experience can be.

Conclusion

As we have repeatedly pointed out, this study is based on an internal data analysis of AI-graded student answers performed by Education Perfect and does not reflect a formal study of learning improvement efficacy. However, even with this caveat, the results are exciting and deserve more exploration and measurement.

Looking at the high points of this study:

• The data is derived from approximately 210,000 answers from 19,500 students over a 10-week period
• There were over 200,000 AI-marked responses, which helped to free teachers up to focus where they were needed most
• 87% of students engaged with the AI to improve low-scoring responses
• This resulted in a 47% average improvement in the quality of final responses based on our star rating
• 69% of students with low-scoring responses demonstrated deeper understanding by their final attempt.

We acknowledge that in the early days of AI technology, AI grading and feedback can sometimes be incorrect. We would also point out that model answers or exemplar responses may have been shown to students between the first and final attempt, however the trends still showed consistent results, which can’t be ignored.

With results like these, developments in this space will continue to happen rapidly, and at Education Perfect our focus remains on ensuring our comprehensive suite of AI tools is carefully designed to enhance student learning, ensure data privacy, and deliver consistent, high-quality outputs.

For more information on Education Perfect, to see a demo of our AI capabilities or to register for a trial, visit educationperfect.com/ai/.

1. Statistics are based on an internal data analysis of AI-graded student answers performed by Education Perfect and do not reflect a formal study of learning improvement efficacy. EP AI star ratings use a generic marking criteria. AI grading and feedback can sometimes be incorrect. Model answers / exemplar responses may have been shown to students between the first and final attempt.
2. Learning loop was turned on partly through a single day, so we discarded 6,000 answers from that day to remove ambiguity in the results.
3. Response quality is measured through our AI star-based system using generic marking criteria where 1-star is low quality and 5-star is high quality.