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GenAI Productivity: Ideas to project proposal 3

Produced using Google Gemini

In two previous posts I looked at using Generative AI to start producing  project ideas and refining one to be the start of the proposal, Previous blog posts
This post is really a slightly different variation on those. Playing with adding a few things in. The example can be repurposed for non-computing student but the example came from a discussion with a BSc Software Engineering student, thinking about project ideas


Example starting prompt in ChatGPT4o
"Produce 5 project ideas suitable for a BSc Software Engineering students for the following topic: "Ambient Assisted Living" The project must suit someone with a strong interest in programming. For each idea produce a 100 word description; justification in 200 words why this is a worthwhile project;
a set of goals; sources of potential data; physical resources needed"

It does produce 5 projects - personally, they do look interesting. Now  selecting a project to expand and add references
"Taking project 4. , Add in appropriate references throughout the whole project idea. Expand the justification to 500 words and the Project Description to 200 words. At the end, add a reference list. Harvard referencing throughout. Referencing should be in every section. Show the whole project idea"


Final output

"

Project Title:

Voice-Controlled Personal Assistant for Dementia Patients

Project Description (200 words):

This project involves designing and developing a voice-controlled personal assistant (VCPA) specifically tailored for individuals living with dementia. The assistant will use natural language processing (NLP) and speech recognition to offer intuitive, speech-based interaction that accommodates cognitive impairments. Features will include answering basic orientation questions (e.g., “What day is it?”, “Where am I?”), issuing routine reminders (e.g., medication, hydration, appointments), and engaging users with conversational prompts for companionship.

Unlike general-purpose devices like Amazon Alexa or Google Assistant, this system will be customized to address the specific needs of dementia users, such as simplified dialogue, memory aids, and emotionally supportive interactions (Bainbridge et al., 2021). The assistant will avoid complex instructions and provide gentle redirection when confusion is detected.

Built using open-source tools (e.g., Mozilla DeepSpeech, Rasa, or Vosk), the system will run on a low-cost device such as a Raspberry Pi and feature a caregiver dashboard for configuring responses and monitoring interaction patterns. The project will focus on low cognitive load design, customisable content, and offline or private data handling to enhance privacy and usability (Topol, 2019).

By developing this assistant, the project will demonstrate practical programming, AI implementation, and human-centered design tailored to assistive technology applications (Blackman et al., 2021).

Justification (500 words):

Dementia, affecting over 55 million people globally (WHO, 2023), presents challenges in memory, orientation, and daily living. As cognitive decline progresses, individuals may forget essential routines, become disoriented, or experience increased social isolation. While caregivers provide vital support, constant supervision is often unfeasible, especially given rising healthcare demands and staffing shortages (Prince et al., 2015). Assistive technologies like voice-controlled personal assistants (VCPAs) offer a promising solution to support independence and reduce caregiver burden.

Commercial solutions such as Amazon Alexa or Google Assistant are not optimised for people with dementia (Kerssens et al., 2015). They typically assume a high degree of digital literacy and short-term memory, and may cause frustration when misunderstood. These devices are also not tailored to emotional or contextual support. Research by Moyle et al. (2018) shows that when digital companions are personalised and accessible, they can improve quality of life and reduce anxiety in dementia patients.

This project addresses a clear gap in existing systems by focusing on cognitive accessibility, emotional reassurance, and task support through naturalistic speech interaction. Simplified command structures, context-based reminders, and a limited response set will be used to reduce cognitive overload, as recommended by Hung et al. (2020). The assistant will use Natural Language Processing (NLP) and dialogue management techniques to provide consistent responses and adapt to individual needs.

From a software engineering perspective, the project encompasses a range of core skills: real-time system design, speech interface development, dialogue management, and user-centered design. NLP models can be implemented using open-source tools such as Rasa or Snips, allowing the student to work with intent classification, entity recognition, and response generation (Vinyals & Le, 2015). Offline voice recognition using Vosk or DeepSpeech supports privacy-preserving development, which is especially important when handling sensitive health-related information (Topol, 2019).

This project is valuable not only technically, but ethically. Developing a technology that genuinely assists vulnerable individuals aligns with calls for “ethically aligned design” in AI (IEEE, 2019). It also provides students with an opportunity to engage in inclusive and person-centered technology creation, reflecting a broader shift towards responsible innovation in the software engineering field (van Wynsberghe, 2016).

Given the growing prevalence of dementia, especially in aging societies, this project is timely and socially impactful. It offers scope for interdisciplinary learning, practical AI development, and contributes meaningfully to the goals of Ambient Assisted Living (AAL). The final product can serve as a prototype for further research or commercial development.

Goals:

  1. Develop a simplified NLP interface for use by dementia patients.

  2. Implement a voice-controlled reminder and question-answer system.

  3. Design a user-friendly caregiver interface for content management.

  4. Enable offline speech recognition for privacy.

  5. Evaluate usability using dementia-friendly design heuristics.

Potential Data Sources:

  • DailyDialog dataset for natural conversation modelling (Li et al., 2017)

  • WHO Dementia Behavioural Guidelines (WHO, 2023)

  • Existing caregiver support scripts from Alzheimer’s Society (Alzheimer’s Society, 2022)

  • Persona-based testing scenarios from academic dementia studies (Hung et al., 2020)

Physical Resources Needed:

  • Raspberry Pi 4 (or equivalent SBC)

  • USB Microphone and Speaker

  • Internet connection for updates (optional if offline models used)

  • PC for training dialogue models and dashboard development

  • External display (for caregiver dashboard)

References:

Alzheimer’s Society. (2022). Dementia guide for carers and family. [online] Available at: https://www.alzheimers.org.uk [Accessed 10 Jun. 2025].

Bainbridge, E., Potts, H.W.W., & Wyatt, J.C. (2021). Digital health for the cognitively impaired: The challenge of inclusivity. Journal of Medical Internet Research, 23(2), e16850.

Blackman, T., et al. (2021). Ambient Assisted Living technologies to support aging in place: Evidence and implications. Ageing & Society, 41(8), 1792–1810.

Hung, L., et al. (2020). The use of intelligent assistive technologies for people with dementia: A scoping review. Journal of Biomedical Informatics, 108, 103484.

IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE Standards Association.

Kerssens, C., et al. (2015). Personalized technology to support older adults with and without cognitive impairment living at home. American Journal of Alzheimer’s Disease & Other Dementias, 30(1), 85–97.

Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017). DailyDialog: A manually labelled multi-turn dialogue dataset. Proceedings of IJCNLP 2017, 986–995.

Moyle, W., et al. (2018). Exploring the effect of companion robots on emotional expression in older adults with dementia. Journal of Gerontological Nursing, 44(7), 35–41.

Prince, M., et al. (2015). World Alzheimer Report 2015: The Global Impact of Dementia. Alzheimer’s Disease International.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

van Wynsberghe, A. (2016). Ethics for robots: How to design a moral machine. Science and Engineering Ethics, 22(2), 415–431.

Vinyals, O. & Le, Q. (2015). A neural conversational model. arXiv preprint arXiv:1506.05869.

WHO. (2023). Dementia. World Health Organization. [online] Available at: https://www.who.int/news-room/fact-sheets/detail/dementia [Accessed 10 Jun. 2025]."



Next stages: go through it and apply the human bit:
  • Are references real or even good? 
    • You must read the ones you include.
  • Could go back in and get it to find sources for physical devices
  • Is it 'doable'?
  • Rewrite for the particular context.
  • Think of it as a starting point, not the end; refining it will make a better bit of work.


All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon

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