MasterFaculty of Computing and Software Engineering

AI-Integrated Mobile Application Development

Welcome to the advanced study of intelligent mobile applications! I am Prof. Dr. Antoine Paris. As a professor and a pioneering force in the field of AI-Integrated Mobile Application Development, I bring a unique blend of engineering expertise and AI insight to the study of mobile technology. I am honored to lead the AI-Integrated Mobile Application Development (M.Sc.) program at Nexier University. My motto is: "Innovating the Mobile Frontier with AI".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the development of sophisticated mobile apps that leverage the full power of on-device and cloud-based AI, specializing in advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, and product management.

Practical focus

Advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, product management.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or mobile development firms
Roles as mobile AI engineers or product managers
Consultancy in advanced AI-integrated mobile application development
Support roles in academic research projects on mobile AI

Career opportunities

Chief Mobile Architect for technology companies or mobile development firms
AI Engineer specializing in mobile AI integration
UX Designer for intelligent mobile applications
Researcher in AI-Integrated Mobile Application Development

Jobs and projects

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design
Developing strategic thinking for AI-integrated mobile application development
Enhancing problem-solving through the analysis of complex mobile development challenges
Critical thinking for a comprehensive and nuanced understanding of AI-Integrated Mobile Application Development

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Advanced mobile development (iOS/Android) and on-device machine learning (CoreML, TensorFlow Lite).
- Gaining expertise in backend development for AI services and UX for smart applications.
- Developing problem-solving abilities for complex Product management.
- Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design at an advanced level.
Skills you build
- Mastering AI-powered techniques for AI-driven UX optimization.
- Applying advanced mobile application development principles to AI integration.
- Interpreting and analyzing complex mobile platforms and their implications for AI features.
- Identifying optimal UX and predicting user engagement.

Listed courses

Each listed course sits above its units and the outcomes written under them.

AI-Integrated Mobile Application Development

01Advanced Mobile Development (iOS/Android)
1. FoundationsFoundations of Advanced Mobile Development (iOS/Android)
  The learner can master advanced practical skills in Advanced mobile development (iOS/Android) and on-device machine learning (CoreML, TensorFlow Lite), as applied to Advanced Mobile Development (iOS/Android).
  The learner can gain expertise in backend development for AI services and UX for smart applications, as applied to Advanced Mobile Development (iOS/Android).
2. MethodsMethods in Advanced Mobile Development (iOS/Android)
  The learner can develop problem-solving abilities for complex Product management, as applied to Advanced Mobile Development (iOS/Android).
  The learner can cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design at an advanced level, as applied to Advanced Mobile Development (iOS/Android).
3. ApplicationApplication of Advanced Mobile Development (iOS/Android)
  The learner can master AI-powered techniques for AI-driven UX optimization, as applied to Advanced Mobile Development (iOS/Android).
  The learner can apply advanced mobile application development principles to AI integration, as applied to Advanced Mobile Development (iOS/Android).
02On-Device Machine Learning and Optimization
1. FoundationsFoundations of On-Device Machine Learning and Optimization
  The learner can interpreting and analyze complex mobile platforms and their implications for AI features, as applied to On-Device Machine Learning and Optimization.
  The learner can identify optimal UX and predicting user engagement, as applied to On-Device Machine Learning and Optimization.
2. MethodsMethods in On-Device Machine Learning and Optimization
  The learner can apply a method from On-Device Machine Learning and Optimization to a documented case.
  The learner can select an appropriate method from On-Device Machine Learning and Optimization for a stated problem.
3. ApplicationApplication of On-Device Machine Learning and Optimization
  The learner can evaluate a practice of On-Device Machine Learning and Optimization against a stated criterion.
  The learner can transfer On-Device Machine Learning and Optimization to a new documented context.
03Backend Development for Mobile AI Services
1. FoundationsFoundations of Backend Development for Mobile AI Services
  The learner can explain the core terms of Backend Development for Mobile AI Services.
  The learner can distinguish related ideas inside Backend Development for Mobile AI Services.
2. MethodsMethods in Backend Development for Mobile AI Services
  The learner can apply a method from Backend Development for Mobile AI Services to a documented case.
  The learner can select an appropriate method from Backend Development for Mobile AI Services for a stated problem.
3. ApplicationApplication of Backend Development for Mobile AI Services
  The learner can evaluate a practice of Backend Development for Mobile AI Services against a stated criterion.
  The learner can transfer Backend Development for Mobile AI Services to a new documented context.
04User Experience (UX) Design for Smart Applications
1. FoundationsFoundations of User Experience (UX) Design for Smart Applications
  The learner can explain the core terms of User Experience (UX) Design for Smart Applications.
  The learner can distinguish related ideas inside User Experience (UX) Design for Smart Applications.
2. MethodsMethods in User Experience (UX) Design for Smart Applications
  The learner can apply a method from User Experience (UX) Design for Smart Applications to a documented case.
  The learner can select an appropriate method from User Experience (UX) Design for Smart Applications for a stated problem.
3. ApplicationApplication of User Experience (UX) Design for Smart Applications
  The learner can evaluate a practice of User Experience (UX) Design for Smart Applications against a stated criterion.
  The learner can transfer User Experience (UX) Design for Smart Applications to a new documented context.
05Product Management for AI-Integrated Mobile Apps
1. FoundationsFoundations of Product Management for AI-Integrated Mobile Apps
  The learner can explain the core terms of Product Management for AI-Integrated Mobile Apps.
  The learner can distinguish related ideas inside Product Management for AI-Integrated Mobile Apps.
2. MethodsMethods in Product Management for AI-Integrated Mobile Apps
  The learner can apply a method from Product Management for AI-Integrated Mobile Apps to a documented case.
  The learner can select an appropriate method from Product Management for AI-Integrated Mobile Apps for a stated problem.
3. ApplicationApplication of Product Management for AI-Integrated Mobile Apps
  The learner can evaluate a practice of Product Management for AI-Integrated Mobile Apps against a stated criterion.
  The learner can transfer Product Management for AI-Integrated Mobile Apps to a new documented context.
06Advanced iOS and Android Development with AI
1. FoundationsFoundations of Advanced iOS and Android Development with AI
  The learner can explain the core terms of Advanced iOS and Android Development with AI.
  The learner can distinguish related ideas inside Advanced iOS and Android Development with AI.
2. MethodsMethods in Advanced iOS and Android Development with AI
  The learner can apply a method from Advanced iOS and Android Development with AI to a documented case.
  The learner can select an appropriate method from Advanced iOS and Android Development with AI for a stated problem.
3. ApplicationApplication of Advanced iOS and Android Development with AI
  The learner can evaluate a practice of Advanced iOS and Android Development with AI against a stated criterion.
  The learner can transfer Advanced iOS and Android Development with AI to a new documented context.
07On-Device Machine Learning and Backend Services for Mobile
1. FoundationsFoundations of On-Device Machine Learning and Backend Services for Mobile
  The learner can explain the core terms of On-Device Machine Learning and Backend Services for Mobile.
  The learner can distinguish related ideas inside On-Device Machine Learning and Backend Services for Mobile.
2. MethodsMethods in On-Device Machine Learning and Backend Services for Mobile
  The learner can apply a method from On-Device Machine Learning and Backend Services for Mobile to a documented case.
  The learner can select an appropriate method from On-Device Machine Learning and Backend Services for Mobile for a stated problem.
3. ApplicationApplication of On-Device Machine Learning and Backend Services for Mobile
  The learner can evaluate a practice of On-Device Machine Learning and Backend Services for Mobile against a stated criterion.
  The learner can transfer On-Device Machine Learning and Backend Services for Mobile to a new documented context.
08UX Design for Intelligent Mobile Applications
1. FoundationsFoundations of UX Design for Intelligent Mobile Applications
  The learner can explain the core terms of UX Design for Intelligent Mobile Applications.
  The learner can distinguish related ideas inside UX Design for Intelligent Mobile Applications.
2. MethodsMethods in UX Design for Intelligent Mobile Applications
  The learner can apply a method from UX Design for Intelligent Mobile Applications to a documented case.
  The learner can select an appropriate method from UX Design for Intelligent Mobile Applications for a stated problem.
3. ApplicationApplication of UX Design for Intelligent Mobile Applications
  The learner can evaluate a practice of UX Design for Intelligent Mobile Applications against a stated criterion.
  The learner can transfer UX Design for Intelligent Mobile Applications to a new documented context.
09Case Studies in AI-Integrated Mobile Application Development
1. FoundationsFoundations of Case Studies in AI-Integrated Mobile Application Development
  The learner can explain the core terms of Case Studies in AI-Integrated Mobile Application Development.
  The learner can distinguish related ideas inside Case Studies in AI-Integrated Mobile Application Development.
2. MethodsMethods in Case Studies in AI-Integrated Mobile Application Development
  The learner can apply a method from Case Studies in AI-Integrated Mobile Application Development to a documented case.
  The learner can select an appropriate method from Case Studies in AI-Integrated Mobile Application Development for a stated problem.
3. ApplicationApplication of Case Studies in AI-Integrated Mobile Application Development
  The learner can evaluate a practice of Case Studies in AI-Integrated Mobile Application Development against a stated criterion.
  The learner can transfer Case Studies in AI-Integrated Mobile Application Development to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the development of sophisticated mobile apps that leverage the full power of on-device and cloud-based AI, specializing in advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, and product management. My work seamlessly integrates software engineering, artificial intelligence, and user experience design. I am widely recognized for my contributions, with publications like "Cloud-Edge Collaboration for Intelligent Mobile Applications" and "Scalable Backend Architectures for Mobile AI Services" listed on these platforms. I hold prestigious memberships as a "Chief Mobile Architect" at a leading tech company (or a equivalent) and a "Keynote Speaker" at Mobile World Congress. My thought leadership is evident through my advanced research on mobile AI architecture, scalable mobile backend services, and the future of intelligent mobile ecosystems, frequently featured in publications like IEEE Transactions on Mobile Computing or ACM Transactions on Intelligent Systems and Technology.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of mobile AI, focusing on Advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, and Product management. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on AI-integrated mobile application development:

Blog Post (Current Academic Topic): "The Converging Future: Cloud-Edge AI for Mobile Innovation." This blog post academically explores the synergy between cloud-based and edge-based AI in mobile application development. It discusses how offloading complex computations to the cloud while performing real-time inference on the device can optimize performance, reduce latency, and enhance the capabilities of mobile AI features. It highlights the architectural considerations and benefits for developing sophisticated, high-performance mobile applications.

Blog Post (Controversial Topic): "The Mobile AI Paradox: When Your Phone Knows You Better Than You Do - The Ethical Dilemmas of Hyper-Personalized Mobile Intelligence." This article provocatively discusses the highly controversial future where advanced AI systems embedded in mobile applications gather and analyze vast amounts of user data, leading to hyper-personalized experiences that anticipate needs and even influence decisions. It questions whether this level of mobile intelligence, despite its convenience, could inadvertently erode user autonomy, create filter bubbles, or lead to unforeseen biases in algorithmic recommendations. It raises profound ethical questions about data sovereignty, algorithmic transparency, and the imperative to ensure human agency in an increasingly intelligent mobile world.

Article: "Scalable Backend Development for AI-Powered Mobile Applications." This article details the principles and best practices for developing robust and scalable backend services that support AI functionalities in mobile applications. It explores architectural patterns for data ingestion, model serving, and API design, ensuring efficient and reliable communication between mobile devices and cloud-based AI services.

Peer-Reviewed Journal Article: "On-Device Machine Learning for Real-Time Mobile Intelligence." Published in the International Journal of Mobile AI, this article presents groundbreaking research on mastering the development of sophisticated mobile apps that leverage the full power of on-device and cloud-based AI. It specializes in advanced mobile development, on-device machine learning (CoreML, TensorFlow Lite), and designing intuitive UX for smart applications, showcasing novel approaches for real-time mobile intelligence.

Book: "Mastering Mobile AI: Architecting Intelligent Applications." This book provides advanced insights into mastering the development of sophisticated mobile apps that leverage the full power of on-device and cloud-based AI. It covers advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, and product management.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of mobile AI:

"Advanced iOS & Android Development with AI Integration" (Technical Manual).

"Optimizing On-Device Machine Learning Models for Mobile" (Research Paper).

"Designing Intuitive User Experiences for AI-Powered Mobile Apps" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": AI-Driven UX Optimizer. When a student designs a mobile application with AI features, I can instantly use the GAF engine to simulate user interactions and analyze the AI's impact on the overall user experience. This tool identifies potential friction points, predicts user engagement, and optimizes the UX for smart applications, ensuring maximum user satisfaction and intuitive AI integration.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": AI Model Compression & Optimization. When students are integrating AI models into mobile applications, I can instantly activate a GAF-powered "AI Model Compression & Optimization" tool. This tool analyzes the size and computational requirements of their AI model, suggests optimal compression techniques (e.g., quantization, pruning), and visualizes the performance benefits for on-device deployment, ensuring efficient and lightweight mobile AI.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Antoine Paris

Meet your professor Open the classroom

AI Super Mentor

Dr. Lerato Nongqawuse

Advanced mobile development (iOS/Android), on-device machine learning (CoreML, TensorFlow Lite), backend development for AI services, UX for smart applications, product management.

Meet your mentor Open the classroom

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Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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AI-Integrated Mobile Application Development

Academic focus

Practical focus

Internships in technology companies or mobile development firms

Roles as mobile AI engineers or product managers

Consultancy in advanced AI-integrated mobile application development

Support roles in academic research projects on mobile AI

Chief Mobile Architect for technology companies or mobile development firms

AI Engineer specializing in mobile AI integration

UX Designer for intelligent mobile applications

Researcher in AI-Integrated Mobile Application Development

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design

Developing strategic thinking for AI-integrated mobile application development

Enhancing problem-solving through the analysis of complex mobile development challenges

Critical thinking for a comprehensive and nuanced understanding of AI-Integrated Mobile Application Development

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Antoine Paris

Dr. Lerato Nongqawuse

Find the program that expands your universe.