Ecolens AI

18

October

2024

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For our assignment we developed an application called Ecolens AI. Ecolens AI is an application that allows eco-conscious consumers to make informed decisions with regards to buying products with a high sustainability score. We developed this application to cater to the ever-growing consumer demand for sustainable products. With the use of Ecolens AI we hope to bridge the gap between intent and action in sustainable purchasing. By integrating Google Cloud Vision API and ChatGPT customers can take a picture of a product, after which they are instantly provided with a sustainability rating of that product. Should the product score relatively low on a certain sustainability metrics (soil, air pollution, etc.) the application will suggest eco-friendly alternatives.

The Problem: Intention-Action Gap in Sustainable Consumption

The issue that lies at the heart of this problem is that consumers, although motivated to make environmentally conscious purchasing decisions, face significant barriers when trying to do so. Misinformation, greenwashing and the time and effort it takes to research the level of product sustainability contributes to the so-called intention-action gap. This phenomenon shows the discrepancy between a consumer’s desire to act sustainably and their actual purchasing behaviour. Despite initiatives like the EU’s Ecolabels, many consumers remain skeptical about sustainability claims due to misleading marketing tactics. Our application is aimed at addressing this issue by making it significantly easier for consumers to find reliable product information.

EcoLens AI: A Solution for Sustainable Shopping

EcoLens AI removes most of the transaction costs associated with researching sustainability. Users can simply scan a product using their phone’s camera, and the application, powered by the Google Cloud Vision API, identifies the product. After this, with the help of the ChatGPT API, it will scan the internet and find all the information related to the earlier detected product. It will find what materials it is made of, the supply chain, the lifetime, and more information to assess how sustainable and eco-friendly the product is. Then, the application lets ChatGPT return an eco-friendliness score (between 0% – 100%) and a reliability score (between 0% – 100%) to show how valid the eco-friendliness score and associated information is. Not only does this remove a lot of transaction costs, but it brings increased convenience by showing the consumer, in a few seconds, how sustainable the product in front of you is. EcoLens AI tackles greenwashing by using verified data from trusted certification bodies such as FairTrade and ISO. It assesses the credibility of product claims and warns users if the information provided is vague or unreliable. By offering users this transparency, EcoLens AI aims to close the intention-action gap and reduce consumer frustration when trying to shop responsibly.

Business Model and Strategic Impact

EcoLens AI operates on an affiliate-based revenue model. It generates income through partnerships with e-commerce platforms like Amazon and Bol.com by recommending more sustainable alternatives to the scanned products. This model not only benefits the app but also encourages manufacturers to improve transparency and sustainability to achieve higher ratings within the app.

Challenges and Future Outlook

An algorithm using artificial intelligence is only as useful as its input data, so having access to accurate and up-to-date data is critical, especially to combat greenwashing. To ensure credibility, EcoLens AI partners with reputable certification bodies and refines its data sources. Additionally, operational costs, like AI maintenance, poses a challenge but is manageable with strategic planning.

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Pros and Cons of AI in Mental Healthcare

11

October

2024

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As much as 20.3% of  college students experience mental health disorders, such as anxiety, depression or substance abuse disorder. Despite this, only 16.4% of students with mental health problems received any form of treatment (Auerbach et al., 2016). Other studies show college students suffer from mental health disorders with numbers as high as 31% (Mortier et al., 2018). Mental health issues like depression and anxiety significantly impair a student’s ability to focus and retain information, which leads to missing classes, failing assignments and higher rates of academic attrition (Eisenberg et al., 2009; Storrie et al., 2010). 

The demand for mental health care practitioners is almost constantly increasing, while time, money, and effort are limited. The ability of AI to help accommodate this high demand has made many people hopeful to receive the mental health care they urgently need. However, there are also drawbacks to replacing real doctors with a robot. One of the main problems is the dehumanization of healthcare; a field traditionally known for its compassionate care. I also noticed that the human dimension of healthcare is invaluable and hard to replace with AI.

Even though the therapist-patient relationship is hard to emulate with the use of AI, AI can still prove beneficial for anyone struggling with mental health issues. Artificial Intelligence can, for example, access relevant patient-related data from various sources and through triangulation come up with an accurate assessment of someone’s mental health status (Walsh et al., 2017). AI is superior to a human when it comes to uncovering patterns in seemingly unrelated datasets.

In conclusion I would say that AI certainly has very promising features to aid mental health practitioners, but it also has very clear shortcomings. I think for now the best course of action in the field of healthcare is to combine the care of a therapist with the insights of AI.

Auerbach, R. P., Alonso, J., Axinn, W. G., Cuijpers, P., Ebert, D. D., Green, J. G., Hwang, I.,    Kessler, R. C., Liu, H., Mortier, P., Nock, M. K., Pinder-Amaker, S., Sampson, N. A., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H., Benjet, C., Caldas-De-Almeida, J. M., Demyttenaere, K., . . . Bruffaerts, R. (2016). Mental disorders among college students in the World Health Organization World Mental Health Surveys. Psychological Medicine, 46(14), 2955–2970. https://doi.org/10.1017/s0033291716001665

Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., Demyttenaere, K., Ebert, D. D., Green, J. G., Hasking, P., Murray, E., Nock, M. K., Pinder-Amaker, S., Sampson, N. A., Stein, D. J., Vilagut, G., Zaslavsky, A. M., & Kessler, R. C. (2018). WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. Journal of Abnormal Psychology, 127(7), 623–638. https://doi.org/10.1037/abn0000362

Eisenberg, D., Golberstein, E., & Hunt, J. B. (2009). Mental health and academic success in college. The B E Journal of Economic Analysis & Policy, 9(1). https://doi.org/10.2202/1935-1682.2191

Storrie, K., Ahern, K., & Tuckett, A. (2010). A systematic review: Students with mental health problems—A growing problem. International Journal of Nursing Practice, 16(1), 1–6. https://doi.org/10.1111/j.1440-172x.2009.01813.x

Walsh CG, Ribeiro JD, Franklin JC (2017) Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci 5:457–469

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From Bro to Pro: AI and AR will turn you into a machine

19

September

2024

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The profound impact Artificial Intelligence (AI) has on the world of business is hard to deny, with clear evidence that AI-tools have the capability of directly and indirectly increasing profits (Basri, 2020). This might stir the heart of many finance departments, but the average gymbro remains unfazed. For them, ever-increasing economic growth takes the back seat to ever-increasing physical growth. Luckily, Artificial Intelligence has also found its way into the world of sports. Bodemer (2023) shows that AI is capable of enhancing an athlete’s individual performance, optimizing their training strategies as well as function as a surrogate-coach for those who do not have the funds to afford a ‘real’ coach. Particularly, those athletes who use wearables such as smart watches are able to reap the benefits of AI-based training programs, resulting in more optimized training loads and recovery strategies, which directly improve training performance and reduce injury risk (Cossich et al., 2023). Besides structured, highly individualized training programs AI also has the ability to provide an athlete with a detailed and well-balanced diet (Bodemer, 2023) which is paramount to making consistent gains.


Where AI can provide valuable data metrics, Augmented Reality (AR) has the potential of overlaying the physical world with digital information (Dargan et al., 2022). This can provide an athlete with real-time feedback on bar path, optimal lifting technique, and live corrections, enhancing their form and reducing injury risk during heavy lifts. With AR, a lifter could visualize data such as speed, force output, and angles of movement, enabling a more precise and immediate understanding of their performance (Cossich et al., 2023).


The use of AI and AR in sports is not the future — it’s here already, and it is accessible to everyone! The days of spinning your wheels and guessing your way through a workout are a thing of the past.

Basri, W. (2020). Examining the impact of Artificial intelligence (AI)-Assisted social Media marketing on the performance of small and medium enterprises: Toward Effective Business Management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), 142. https://doi.org/10.2991/ijcis.d.200127.002
Bodemer, O. (2023). Enhancing Individual Sports Training through Artificial Intelligence: A Comprehensive Review. Eng OA, 1(2), 111-119.
Cossich, V. R. A., Carlgren, D., Holash, R. J., & Katz, L. (2023). Technological breakthroughs in sport: current practice and future potential of artificial intelligence, virtual reality, augmented reality, and modern data visualization in performance analysis. Applied Sciences, 13(23), 12965. https://doi.org/10.3390/app132312965
Dargan, S., Bansal, S., Kumar, M., Mittal, A., & Kumar, K. (2022). Augmented Reality: A comprehensive review. Archives of Computational Methods in Engineering, 30(2), 1057–1080. https://doi.org/10.1007/s11831-022-09831-7

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