FlipPraxis helps universities move from traditional teaching to experiential, outcome-driven and industry-aligned learning, at scale.
Transform classroom learning into measurable graduate capability through Project-Based Learning, Active Learning, Flipped Classrooms and Industry-Integrated Projects.
Start with a focused pilot. Measure the impact. Scale what works.
ReadyScan · Free readiness assessment
Take ReadyScan, our University Transformation Readiness Assessment, and identify the key gaps affecting graduate capability, experiential learning and employability.
You receive
Your Transformation Readiness Score + Pre-Learn Maturity Index + Key Gaps + a downloadable PDF report15 statements and a short institutional profile. About 8 minutes.
The institutional shifts FlipPraxis helps universities make, from the classroom to the whole programme.
Move beyond content delivery toward active, applied learning.
Give students opportunities to solve meaningful problems.
Develop skills students can demonstrate, not simply claim.
Build career readiness throughout the academic journey.
Create a scalable model that faculty and academic leaders can implement across programmes.
We don't sell a platform. We help universities transform learning into measurable graduate capability.
We don't stop at recommendations.
We connect classroom transformation with graduate capability.
Transformation works only when both sides are supported.
Start focused, measure impact and expand systematically.
Technology enables the model; transformation remains the objective.
Designed for university-level implementation, not isolated classroom experiments.
The APEX Learning Cycle
The future of higher education is not simply about what students know. It is about what they can apply, create and demonstrate.
Build the foundational knowledge.
Apply knowledge to problems, projects and real situations.
Reflect, receive feedback and demonstrate capability.
Transfer learning into new contexts, challenges and professional environments.
FlipPraxis helps universities embed APEX across the learning journey.
Before class, students work through short videos, focused readings and quick checks, so they arrive ready to use what they have learned rather than hear it for the first time.
Contact time is spent on application: team projects, industry briefs, cases and design challenges, with faculty and mentors coaching rather than presenting.
Peer review, faculty assessment against shared rubrics and feedback from industry partners show students where they are strong and where to improve.
Each cycle ends by applying the learning to a new context. What students learned from feedback shapes the next challenge, so capability builds semester on semester.
Solutions
Four connected pillars that move a programme from content delivery to demonstrated capability.
Connect learning with meaningful challenges aligned with students' strengths, interests and career pathways.
Transform classroom time into opportunities for collaboration, problem-solving, discussion, application and reflection.
Move foundational learning before class and use classroom time for application, mentoring, coaching and deeper learning.
Connect students with real-world problems that build portfolios, professional capability and employer networks.
Pillar 01
Students work on multidisciplinary, credit-aligned projects built around authentic challenges. They investigate problems, analyse evidence, design and test solutions and present their work, with projects matched to their strengths and the careers they are working towards.
Pillar 02
Students learn more when they are doing the thinking. We help faculty turn classroom time into structured collaboration, discussion, problem-solving and reflection, with practical session designs they can run from the next class onwards.
Pillar 03
Foundational content moves before class through videos, readings, podcasts and digital resources. Class time is then used for discussion, collaborative work, project work, mentoring and practical application, and faculty move from delivering content to facilitating learning.
Pillar 04
Students work on live problems with real organisations: consulting assignments, innovation challenges, startup engagements and social impact projects. They graduate with a portfolio of work, professional references and an employer network built during study.
Every engagement is planned around outcomes the institution can track and report.
Build demonstrable future-ready skills.
Increase meaningful participation in learning.
Support faculty in implementing active and experiential learning.
Bring live industry problems into learning so graduates leave with workplace capability employers can see.
Generate evidence of learning outcomes and continuous improvement.
Strengthen the evidence behind what rankers weigh: graduation outcomes, employer perception and teaching quality.
Why rankings belong here: graduation outcomes carry 20 to 25% of an NIRF score and perception a further 10%; QS gives 20% to employer reputation and employment outcomes; THE gives 29.5% to the teaching environment. Sources: NIRF 2025, QS, THE. Rankings depend on many factors; FlipPraxis strengthens the evidence behind them and does not promise a rank.
The Launchpad Pilot
Universities don't need to transform everything at once. FlipPraxis enables institutions to begin with the Launchpad Pilot, a focused cohort of about 60 learners, then demonstrate measurable impact and build the evidence required for wider implementation.
Identify the learning and capability gaps.
Create the right transformation model for the programme.
Implement with a focused student cohort.
Capture learning, engagement and capability evidence.
Expand what works across programmes and departments with Scale Studio.
Pilot investment is determined by cohort size, programme scope and institutional requirements.
Transformation involves the whole institution. Each team gets support that fits its role.
Evidence
Our evidence library brings together what happens when programmes adopt the FlipPraxis model. We publish material as engagements are completed and approved by partner institutions, and we don't make claims we can't support.
Each FlipPraxis case study follows the same structure, so leaders can compare contexts and see what is replicable.
FlipPraxis Apex Hub
FlipPraxis Apex Hub is the platform that supports faculty and programme teams in running the model day to day, with team formation, feedback, analytics and evidence in one place. It is the enabler of transformation, not the product being sold.
Match students to projects based on skills, preferences and goals.
Track project milestones and flag overdue deliverables across a programme.
Structured peer assessment with rubrics and faculty oversight.
Assign pre-class material and see who is prepared before class starts.
Programme dashboards and accreditation-ready exports.
Every project, deliverable and outcome captured each semester.
Works with Canvas, Moodle and Blackboard, with SSO via SAML or OAuth.
Role-based access, NDA handling and institutional data ownership.
Budget tracking and invoicing for fee-based client projects.
Thinking on higher education transformation, the future of work and experiential learning for university leaders and faculty.
Optimistic but unsure they are prepared, and questioning the value of a degree: what international and US research says Gen Z students want, and what universities can change.
Faculty can redesign a course. Only leadership can make the decisions that let new ways of teaching outlast the first enthusiastic champion.
Most students now study with AI. What a global survey of university students shows, and why applied, observed learning is the strongest response.
A short due-diligence checklist for university leaders, deans and quality teams reviewing proposals for project-based, active or industry-integrated learning.
How institutions connect students with live industry projects, consulting assignments and innovation challenges, and the operating model that keeps it sustainable.
What the research says about which active learning techniques improve outcomes, and the design features they share.
A phased guide for faculty moving from lecture-based delivery to a flipped classroom built on the APEX cycle, and what the research says about getting the design right.
Analytical thinking, resilience, leadership, creative thinking, technological literacy and lifelong learning: what the World Economic Forum's 2025 employer survey says, and how to build these skills on purpose.
The Diagnose, Design, Pilot, Measure and Scale sequence for institution-wide change.
At least five recent, independent sources for each theme, chosen because they inform FlipPraxis programmes. Each link opens the original publisher.
Tertiary attainment is at a record high, yet only about 43% of bachelor’s students finish on time and some graduates still lack baseline skills: access alone no longer guarantees outcomes.
Supports FlipPraxis Compass, ReadyScan
A global expert panel names AI tools for teaching and learning and evolving teaching practices among the key developments reshaping how learning is designed, documented and valued.
Supports FlipPraxis Catalyst, FlipPraxis Apex Hub
Institutions are scored on teaching, learning and resources, research, graduation outcomes, outreach and perception, with graduation outcomes weighted at 25% for colleges.
Supports ProofFolio, FlipPraxis Compass
One fifth of the score comes from the employability and outcomes lens: employer reputation (15%) and employment outcomes (5%).
Supports Talent Bridge, ProofFolio
NAAC is replacing letter grades with a basic (binary) accreditation decision followed by maturity-based graded levels, recommended by the Radhakrishnan Committee.
Supports ProofFolio, Scale Studio
Employers expect 39% of workers’ core skills to change by 2030, and 63% name skill gaps as the biggest barrier to transforming their business.
Supports Pathways, Spark
National employability rose to 56.35%, which means more than four in ten candidates assessed are not yet job-ready.
Supports Pathways, Talent Bridge
82% of Indian employers report difficulty finding skilled talent, above the 72% global average, with AI skills now the hardest to find.
Supports Talent Bridge, Bridge
70% of employers now use skills-based hiring, and they want candidates to show evidence of problem-solving, communication and teamwork.
Supports ProofFolio, Pathways
Employers want examples, not lists, of teamwork and problem-solving, and consistently prefer candidates with internship experience.
Supports Bridge, ProofFolio
Draws on more than 23,000 Gen Z and millennial respondents in 44 countries, with a section re-examining how young people judge the value of higher education.
Supports Pathways, Launchpad Pilot
Students who see a clear link between their coursework and their future feel markedly more prepared for what comes next.
Supports Pathways, Spark
Students and recent graduates remain confident their degree builds career-relevant skills, even as wider public confidence lags.
Supports Bridge, Talent Bridge
Student use of AI tools jumped from 66% to 92% in a year, and 88% have used generative AI for assessments.
Supports Flip Forward, Educator Edge
86% of students across 16 countries use AI in their studies, and 80% say their university’s integration of AI does not yet fully meet their expectations.
Supports Spark, Educator Edge
Across 134 studies, active learning raised exam performance by about half a standard deviation, with larger gains when more class time is active.
Supports Spark, Educator Edge
Synthesising 25 meta-analyses, flipped classrooms had a moderate positive effect on student outcomes, with results depending on design and context.
Supports Flip Forward
Across 66 experimental and quasi-experimental studies, project-based learning improved academic achievement, attitudes and thinking skills.
Supports Pathways
A recent systematic review of how universities embed workplace learning into programmes and what it contributes to graduate readiness.
Supports Bridge, Talent Bridge
Stakeholders value employability activities, but the evidence base is thin: the authors call for robust tracking of students into work.
Supports ProofFolio, Launchpad Pilot
Technology literacy for the future workforce sits alongside data and AI on the list of priorities for higher education leaders in the year ahead.
Supports FlipPraxis Apex Hub, Scale Studio
71% of presidents now have an institution-wide AI task force or strategy, and half launched public trust initiatives built on demonstrating student value.
Supports FlipPraxis Compass, ProofFolio
Most leaders say students already use AI widely, yet a majority feel their institution is not ready to prepare graduates for AI-driven work.
Supports FlipPraxis Compass, Educator Edge
Faculty worry about student over-reliance on AI, and many say their institutions have not prepared them to teach with it: a leadership and faculty-development gap.
Supports Educator Edge
Teaching (the learning environment) is the largest single pillar at 29.5%, with teaching reputation alone worth 15% and industry engagement scored separately.
Supports FlipPraxis Compass, Educator Edge
Strategy conversation
Tell us where your institution is today and what you want to change. We will come to the conversation prepared with relevant models, pilot options and questions specific to your context.
All fields are required.
We use these details only to respond to your request. See our .
Most class time is spent on discussion, problem-solving and application rather than lecture.
Students regularly learn through projects, cases or simulations tied to real contexts.
Foundational content is available before class so contact time can be used for application.
Redesign one high-enrolment course so that class time is used for application, starting with a flipped module and structured active learning sessions.
Programme outcomes explicitly name capabilities such as critical thinking, collaboration and leadership.
Students are taught and assessed on these capabilities, not only on subject knowledge.
Graduates can show evidence of capability through portfolios, project outputs or employer feedback.
Define a capability framework for one programme and build it into assessment rubrics, so students graduate with evidence they can show employers.
Students work on live problems sourced from employers or community organisations.
Employers are involved in designing, mentoring or assessing student work.
Programmes are reviewed against changing workplace expectations at least once a year.
Set up a small, managed Talent Bridge pipeline of industry projects for one cohort, with clear scoping, a faculty contact per project and employer feedback built in.
Faculty have been trained in active, project-based or flipped teaching approaches.
Faculty have time, resources and support to redesign their courses.
There is a community of practice where faculty share what works.
Start with an Educator Edge faculty cohort for the pilot: practical training, ready-to-use session designs and coaching through the first semester.
Course and programme outcomes are mapped (OBE) and assessed systematically.
You can produce accreditation-ready evidence of learning outcomes without a last-minute scramble.
Data on engagement and capability is reviewed and used to improve programmes.
Agree pilot success measures in advance and capture evidence throughout the semester in ProofFolio, mapped to your OBE and accreditation requirements.
A short profile so your report reads your results in the context of your country, size and disciplines.
Pre-Learn is the content students work through before class, so contact time can go to application. These answers produce your Pre-Learn Maturity Index.
What stands in the way, what you are accountable to and how soon you want to move. These shape the roadmap in your report.
Country
Type of institution
Public or state university Private university Autonomous or deemed-to-be university Affiliated or constituent college Business or professional school Technical, polytechnic or community college OtherTotal student enrolment
Under 2,000 2,000 to 10,000 10,000 to 30,000 Over 30,000Main discipline areas
Choose up to three.
Engineering and technology Business and management Medicine and health sciences Natural and computer sciences Arts, humanities and social sciences Law Education Design and architecture MultidisciplinaryYour role in academic decisions
Institutional leadership (VC, President, Provost, Registrar) Dean, director or head of department Quality, IQAC or accreditation Learning design or educational technology Faculty member OtherType of Pre-Learn content provided to students
Select every format your faculty use today, even in a few courses.
PPT or slide decks Hand notes or typed lecture notes E-books, articles or reading packs Videos recorded by faculty Curated open videos (YouTube, MOOCs) Podcasts or audio lessons In-video questions Pre-class quizzes or self-checks Simulations or virtual labs AI avatar videos AI tutor or chatbot for pre-class questions Adaptive or personalised learning paths No structured Pre-Learn content yetShare of courses with structured Pre-Learn
None Under 10% 10% to 30% 30% to 60% Over 60%How do you know students completed their Pre-Learn?
We do not track it A verbal or show-of-hands check in class LMS views or downloads Graded pre-class quiz or in-video questions Analytics dashboard with follow-up for non-completersTypical Pre-Learn completion rate
Below 25% 25% to 50% 50% to 75% Above 75% Not measuredMain learning platform (LMS)
Moodle Canvas Blackboard D2L Brightspace Google Classroom Microsoft Teams In-house or other platform No institution-wide LMSYour institution's position on generative AI in teaching
Not addressed yet Individual faculty experiment informally Published guidelines for staff and students Institution-wide strategy with faculty trainingBiggest barriers to changing how teaching works
Choose up to three.
Faculty time and workload Faculty skills and confidence Large classes or rigid timetables Examination and assessment regulations Student readiness or resistance Classrooms and digital infrastructure Budget Leadership alignment Measuring the impactAccreditation and ranking frameworks you report to
Select all that apply.
NAAC NBA NIRF ABET AACSB EQUIS or AMBA National quality agency (QAA, TEQSA, CHEA or similar) QS or THE rankings None at presentWhen do you plan to act?
Exploring options Start within 6 months Start within 12 months Already under way, ready to scaleThe one outcome you most want to improve in the next two years
Live industry projects are among the most valuable experiences a university can offer, but they rarely succeed as a one-off assignment. Kuh (2008) lists internships and capstone projects among the “high-impact practices” associated with deeper learning and persistence, with an important condition: they only have that impact when they are done well. Institutions that sustain live-project learning treat it as operating infrastructure, not an occasional favour from a friendly employer.
Relying on one or two employer relationships collapses the moment a champion changes jobs. A durable pipeline draws on alumni networks, regional business associations, public bodies and non-profits, with a simple intake process that lets any organisation propose a challenge.
Live problems are messy by nature. Translate open-ended challenges into milestones that fit the academic calendar: problem framing, stakeholder interviews, prototype or analysis, and a client-facing presentation. Faculty can then assess rigorously without managing the client relationship day to day.
The substance of the work matters. In the National Association of Colleges and Employers’ survey of graduating US seniors in 2019, 57.5% of those who had completed an internship had received a job offer, compared with 43.7% of those who had not, and paid internships, where students reported doing more substantive professional work, performed better still. The lesson applies anywhere: students benefit most when they do real work for real clients, and leave with concrete outputs to discuss with employers.
“Active learning” describes everything from a two-minute pair discussion to a full problem-based curriculum, which makes the evidence easy to misread. The overall case is strong: in a meta-analysis of 225 studies of undergraduate science, engineering and mathematics courses, Freeman et al. (2014) found that exam performance improved by almost half a standard deviation under active learning, and that failure rates were 55% higher under traditional lecturing. The techniques with the most consistent support share a few design features.
Dunlosky et al. (2013) reviewed ten common learning techniques. Practice testing and spacing practice over time received the highest utility ratings, while re-reading and highlighting were rated low. Short, low-stakes retrieval at the start or end of each session is one of the simplest changes a lecturer can make.
Smith et al. (2009) showed that after discussing a concept question with peers, students did better on a new question about the same concept, even when no one in the group had known the answer at first. The gains were largest for the hardest questions, so discussion prompts should be difficult enough to produce disagreement.
Hattie and Timperley (2007) found feedback to be one of the most powerful influences on learning, but its effect depends on the kind of feedback given. Feedback about the task and how to improve helps far more than praise. Frequent low-stakes checks give students information while they can still use it.
In randomised physics classes at Harvard, Deslauriers et al. (2019) found that students learned more from active learning but felt they had learned less, because the extra effort felt like confusion. Explaining this to students early protects engagement.
Programmes that add active techniques without changing assessment often see engagement rise while results stay flat, because examinations still reward memorisation. Lasting gains require assessment to change alongside the classroom.
Flipping a classroom is less about recording lecture videos and more about redesigning what happens during contact time. Two large meta-analyses published in 2019 found that, on average, flipped classrooms produce a small improvement in learning compared with traditional teaching (van Alten et al., 2019; Låg & Sæle, 2019). The design details explain much of the difference between courses that benefit and courses that do not.
Pick the module students have historically found hardest. Move the foundational content into short pre-class materials and use the contact time for applied problem-solving.
This matters: van Alten et al. found larger learning gains when quizzes were added to flipped courses.
The same study found better results when face-to-face time was kept rather than reduced. Class time should default to case work, problem sets or industry-style challenges built on the APEX cycle, with faculty circulating as coaches rather than presenting from the front.
Faculty often worry they will cover less. The answer lies in the design: foundational content moves to short pre-class materials, and contact time is used for the application that lectures rarely leave time for. Flipping is not a way to save teaching time; it works when class time is redesigned, not when it is cut.
The World Economic Forum’s Future of Jobs Report 2025 draws on more than 1,000 employers representing over 14 million workers across 55 economies. Employers expect 39% of workers’ core skills to change between 2025 and 2030. Analytical thinking remains the most sought-after core skill, considered essential by seven out of ten companies. The question for universities is not which skills matter, but how to build them deliberately rather than hoping they emerge as a by-product of a degree.
Traditional lecture and examination sequences reward individual recall under controlled conditions. These six skills develop under almost the opposite conditions: ambiguous problems, teamwork, real stakes and repeated feedback.
Project-Based Personalized Learning gives students repeated, assessed practice in all six. The evidence for project-based learning is encouraging: a meta-analysis of 30 studies covering 12,585 students in nine countries found a medium-to-large positive effect on academic achievement compared with traditional instruction (Chen & Yang, 2019). Map each skill to a project stage and assess it with a shared rubric, so capability is visible rather than assumed.
Institution-wide transformation almost never starts institution-wide. Programmes that reach scale follow a deliberate Diagnose, Design, Pilot, Measure and Scale sequence rather than attempting a big-bang rollout. The reason is quality: high-impact practices only deliver their benefits when they are done well (Kuh, 2008), and doing them well takes practice.
Before changing anything, map where students disengage, where employer feedback is weakest, and which faculty are already experimenting with active methods. This becomes the evidence base for the pilot proposal.
Design a single pilot with a focused cohort, for example around 60 students across two or three courses, with success measures agreed in advance: engagement, assessed capability, and employer and faculty feedback.
Review the results against the agreed measures and decide whether to scale, adapt or stop.
Scaling succeeds when new faculty inherit the pilot playbook, not only the enthusiasm behind it: documented rubrics, onboarding materials and a support structure that does not depend on the original champion. Research on faculty change consistently identifies lack of training, time and incentives as the main barriers (Brownell & Tanner, 2012), so the scale-up plan should budget for all three.
The evidence for active learning is strong (Freeman et al., 2014), and most universities already have faculty who teach through projects, discussion and real problems. What decides whether that practice spreads, or quietly disappears when those faculty move on, is a small set of decisions that sit above any single course. They belong to the Vice-Chancellor or President, the Provost and the Deans.
“Active learning” is a method, not a goal. Leaders who succeed name the institutional outcome first: graduates who can demonstrate capability, stronger employer relationships, or accreditation evidence collected as teaching happens. A clear purpose tells faculty why the extra effort matters and gives the leadership team a way to judge progress.
Research on why faculty do not change how they teach points again and again to the same barriers: lack of training, time and incentives (Brownell & Tanner, 2012). Leaders can address all three directly:
Students take seriously what is assessed. If examinations still reward recall, project work will be treated as optional. Academic boards can allow project outputs, presentations, peer review and rubric-based assessment of capabilities to count towards grades, mapped to programme learning outcomes.
When industry projects depend on one faculty member’s contacts, they end when that person leaves. A central owner for partnerships, standard agreements covering confidentiality and intellectual property, and one point of contact for employers turn goodwill into a lasting pipeline of real problems.
High-impact practices deliver when they are done well (Kuh, 2008). Before a pilot starts, agree what success looks like, who will review the results and when. That turns the next decision, whether to scale, adapt or stop, into a matter of evidence rather than opinion.
Proposals for project-based learning, flipped classrooms or industry projects often arrive with enthusiasm and little detail. The research is clear that design and execution decide the outcome: high-impact practices deliver when they are done well (Kuh, 2008), and flipped designs work better when class time is protected and preparation is checked (van Alten et al., 2019). These questions help leaders judge whether a proposal is ready for a pilot.
A proposal that answers most of these clearly is ready for a pilot. One that cannot is not a failure: the unanswered questions show exactly where to start.
Gen Z, broadly those born between the late 1990s and the early 2010s, now fills most undergraduate classrooms. Large international and national studies point the same way: young people want learning that is clearly connected to their futures, and many are not sure they are getting it.
Deloitte’s 2025 survey of 23,482 Gen Zs and millennials across 44 countries found that 31% of Gen Z respondents had decided not to pursue higher education. Concerns about the quality of education and its relevance to the job market featured among the reasons respondents gave.
In the United States, Gallup and the Walton Family Foundation’s 2024 Voices of Gen Z study found that only about half of Gen Z (51%) felt prepared for the future. Engagement was the strongest predictor: the most engaged school students were ten times more likely than the least engaged to strongly agree that they felt prepared.
The Gallup studies survey young people in the United States, many of them school students about to enter higher education; the Deloitte survey covers 44 countries. Universities should test these patterns with their own students, which is what a readiness assessment and a focused pilot are designed to do.
Generative AI is now part of how students learn. The Digital Education Council’s 2024 Global AI Student Survey of 3,839 university students in 16 countries found that 86% use AI in their studies and 54% use it at least weekly. Yet about half did not feel ready to use AI well, and 80% said their university’s integration of AI did not fully meet their expectations.
Tasks that only ask students to reproduce information, such as summaries, standard essays and recall questions, are the tasks AI tools complete most easily. Assessment built only on those tasks now tells a university less about what a student can actually do.
Projects, live problems, presentations and peer discussion ask students to make judgements, explain their reasoning and respond to questions in real time. These methods also have a strong evidence base: active learning improves exam performance and reduces failure rates compared with traditional lecturing (Freeman et al., 2014). Employers want both kinds of capability: the World Economic Forum’s Future of Jobs Report 2025 lists AI and big data as the fastest-growing skills, alongside analytical and creative thinking.
Identify the gaps between teaching, capability and employability.
FlipPraxis | APEX in Learning. Impact in Action.
These Terms govern your institution's engagement with FlipPraxis transformation services - including consulting, implementation, faculty enrichment, and advisory. By engaging FlipPraxis, your institution agrees to these Terms.
FlipPraxis is a strategic education transformation partner - not a software vendor. Our services include:
Each engagement is governed by a separate Statement of Work (SOW) specifying scope, deliverables, timelines and fees. These Terms apply alongside that agreement.
We want to be unambiguous about this:
All FlipPraxis frameworks, methodologies and tools - including the APEX Learning Model, PBPL deployment guides, transformation roadmaps, rubrics and training materials - are and remain the intellectual property of FlipPraxis. Your institution may use these during the engagement for internal transformation purposes.
All curriculum content, programme designs, student work, employer relationships and materials created by your faculty, staff or students remain your institution's property. We do not claim ownership of anything your institution creates.
Where materials are jointly created (e.g. customised rubrics, programme blueprints), ownership will be specified in the relevant SOW. In the absence of specific terms, the institution retains full rights to use co-created materials.
Both parties agree to hold the other's confidential information in strict confidence - including institutional strategies, financial data, student information, FlipPraxis methodologies and pricing. This obligation survives contract termination for three years.
Institutions agree not to:
FlipPraxis warrants that services will be delivered with professional skill and care. We do not warrant specific measurable outcomes (e.g. exact placement rate improvements) - these depend on institutional commitment, faculty engagement, and factors outside our control. Our liability is limited to fees paid for the relevant engagement in the preceding six months.
Engagements begin on SOW execution and run for the agreed period. Either party may terminate for material breach with 30 days written notice. FlipPraxis may pause services for non-payment. On exit, your institution retains all co-created materials and data.
These Terms are governed by the laws of India. Disputes are resolved first through good-faith negotiation, then binding arbitration under applicable law.
We may update these Terms. Active institutional clients will be notified at least 30 days before any material change takes effect.
We are happy to walk through any section before you engage. Reach out via our contact form and we'll get back to you.
FlipPraxis is committed to protecting the privacy of every institution, faculty member, student and individual who engages with our services. This policy explains exactly what we collect, how we use it, and the rights you hold.
Where student data is involved in a programme engagement, your institution is the data controller and FlipPraxis acts as a data processor. This means:
Employer contacts and industry partnerships developed through FlipPraxis-facilitated programmes belong entirely to your institution. We will never contact, market to, or share data about your employer partners without explicit written permission.
We use information only to:
You have the right to:
To exercise any right, submit a request via our contact form. We respond within 30 days.
flippraxis.com uses browser storage in two ways. Essential storage remembers your cookie choice and keeps forms working; it is always on. Visit statistics storage (a random visitor identifier) is used only if you choose Accept all in the cookie banner. You can change your choice at any time using Cookie settings in the footer, and choosing Essential only deletes the identifier. We do not use advertising cookies, retargeting or behavioural tracking. Visit records are kept for up to 12 months and are visible only to FlipPraxis administrators.
We use a limited number of third-party tools to support service delivery (e.g. video conferencing, email). These are selected for privacy compliance and used solely within the scope of service delivery. We never share institutional or personal data with third parties for commercial purposes.
We may update this policy periodically. Active institutional clients will be notified by email at least 30 days before material changes take effect. The current version is always at flippraxis.com.
FlipPraxis Privacy Team
Website: flippraxis.com