Asking AI to suggest a Housing Studies research theme and proposal

Asking AI to suggest a Housing Studies research theme and proposal: on AI applications (information from perplexity.ai)

My question 1: "Please suggest a Housing Studies dissertation research theme, via the pragmatism lens, based on the following key words: AI applications, public housing estates in Hong Kong, quality of residential life improvement."

The reply: "A pragmatist research theme in Housing Studies could explore how AI-driven technologies practically enhance residential life quality in Hong Kong's public rental housing (PRH) estates, focusing on actionable outcomes like safety, maintenance efficiency, and resident well-being.

Proposed Theme

"Evaluating Pragmatic AI Interventions for Improving Quality of Life in Hong Kong Public Housing Estates: From Smart Monitoring to Resident-Centric Design."

This theme aligns with pragmatism by prioritizing "what works" in real-world applications—testing AI tools against measurable improvements in daily living, rather than abstract ideals. It draws on ongoing pilots like AI video analytics for detecting hazards (e.g., objects thrown from heights) and IoT sensors for maintenance, aiming to generate evidence-based policy tweaks.

Pragmatist Lens Justification

Pragmatism views knowledge as instrumental for solving practical problems, blending methods to yield usable results. Here, it justifies mixed-methods research: quantitative metrics (e.g., incident response times, resident satisfaction scores) paired with qualitative resident feedback on AI's lived impact. The goal is actionable insights, such as scaling successful pilots (e.g., robots for cleaning/security) while addressing gaps like elderly accessibility.

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Key Research Components

• Objectives: Assess current AI uses (e.g., CCTV anomaly detection, predictive maintenance); identify resident-perceived benefits/drawbacks; propose hybrid AI designs for estates like Hoi Ying.

• Questions:

  • How do AI applications (e.g., real-time monitoring) reduce hazards and boost security in PRH common areas?

  • What resident experiences reveal AI's role in elevating quality of life (e.g., via chatbots or door sensors for seniors)?

  • Which pragmatic interventions optimize AI for Hong Kong's dense estates?

• Methods: Surveys/interviews with tenants/staff in pilot estates (10+ sites); data analysis of HA metrics; prototypes for AI-enhanced communal spaces.

Expected Contributions

This dissertation offers Hong Kong-specific recommendations, like integrating AI with universal design for aging-in-place, supporting HA's "Housing·I&T" push. It advances pragmatism in Housing Studies by linking tech adoption to tangible livability gains, informing scalable upgrades amid housing pressures."

My question 2: "Based on the proposed research theme , please offer a part-time Housing Studies dissertation proposal with the following information: 

1. Dissertation report title

2. 3 research issues (including research gaps) and 3 Hong Kong public media reported issues.

3. 3 research objectives (notably 1 objective being on developing a business decline model for the fast casual restaurant business) and 3 research questions

4. 2 primary research methods and 1 secondary research method

5. 4 academic theories related to Housing Studies and AI applications in housing, (with a reference list in Harvard reference format)

6. A brief justification of the dissertation proposal in terms of academic, practical and originality value."."

The reply: "Pragmatism guides this dissertation proposal by emphasizing practical AI solutions for enhancing residential life in Hong Kong's public housing estates, building on the proposed theme of evaluating AI interventions.

Dissertation Title

Pragmatic Evaluation of AI-Driven Interventions for Enhancing Quality of Residential Life in Hong Kong Public Rental Housing Estates

Research Issues and Media-Reported Problems

Research Issues (with Gaps):

• Limited empirical evidence on resident-perceived impacts of AI tools (e.g., CCTV analytics, IoT sensors) on daily livability, creating a gap in linking tech deployment to subjective well-being outcomes.

• Insufficient integration of resident feedback in AI design for dense estates, overlooking cultural and aging demographics in Hong Kong's context.

• Underdeveloped frameworks for scaling pilot AI projects (e.g., drones for maintenance) amid resource constraints, lacking cost-benefit analyses for long-term adoption.
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Hong Kong Public Media-Reported Issues:

• Persistent illegal parking blocking emergency access in estates, now targeted by AI cameras but with resident concerns over enforcement speed (The Standard, 2025).
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• Building defects like wall cracks delaying maintenance, highlighted in drone-AI pilots yet needing faster rollout (Navigator Insurance blog, 2025).
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• Security hazards from thrown objects, addressed via AI CCTV but sparking privacy debates in media coverage (SCMP, 2025).
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Research Objectives and Questions

Objectives:

• To assess the practical effectiveness of existing AI applications (e.g., monitoring systems) in improving safety and maintenance in pilot estates.

• To explore resident experiences and barriers to AI-enhanced living environments through mixed-methods data.

• To develop a predictive business decline model for on-site fast casual restaurant businesses in public housing estates, forecasting viability declines due to AI-driven changes in footfall and communal space usage (e.g., via regression analysis of sales data against AI deployment timelines).

Questions:

• How do AI tools like real-time analytics measurably reduce hazards and elevate security in Hong Kong PRH common areas?

• What resident feedback reveals AI's tangible effects on quality of life, including for vulnerable groups like seniors?

• Which factors predict business decline for fast casual restaurants in AI-upgraded estates, and how can pragmatic models mitigate this?

Research Methods

Primary Methods:

• Mixed-methods surveys and semi-structured interviews with 200+ residents/staff across 10 pilot estates (e.g., Hoi Ying, Yan Tin) to capture quantitative satisfaction scores and qualitative insights.
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• Field observations and AI system log analysis in live pilots, measuring metrics like response times and incident rates.

Secondary Method:

• Archival review of Housing Authority reports, media articles, and performance data from 2025-2026 smart estate initiatives.

Relevant Theories

Four theories underpin this study, blending Housing Studies with AI applications:

• Pragmatism (Dewey, 1938): Frames research as testing "what works" for livability via iterative AI trials.
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• Smart City Theory (Batty, 2013): Examines AI as networked infrastructure for urban housing efficiency.

• Socio-Technical Systems Theory (Trist, 1981): Analyzes human-AI interactions in estates, balancing tech with social needs.

• Universal Design Theory (Mace, 1985): Guides AI adaptations for inclusive residential quality, e.g., senior-friendly sensors.

Harvard Reference List:

Batty, M. (2013) The new science of cities. MIT Press.

Dewey, J. (1938) Logic: The theory of inquiry. Henry Holt and Company.

Mace, R. (1985) 'Universal design: Barrier free environments for everyone', Designers West, 33(1), pp. 147-152.

Trist, E. (1981) 'The evolution of socio-technical systems', Occasional Paper, No. 2, Ontario Quality of Working Life Centre.

Proposal Justification

Academic Value: Advances Housing Studies by applying pragmatism to AI, filling gaps in mixed-methods evaluations of smart estates and introducing predictive modeling for ancillary businesses.

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Practical Value: Delivers HA-actionable recommendations, like optimized AI rollouts, supporting 2026/27 corporate plans for 10+ pilots amid housing shortages.

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Originality Value: Pioneers a business decline model tailored to fast casual outlets in AI-transformed estates, offering novel insights into economic ripple effects on residential ecosystems."

A collection of blog notes on using chatgpt for research purpose.