PRE2024 3 Group16

Group members

Name	Studentnumbers
Tim Hobbes	1798251
Eline Smit	1903071
Veselin Todorov	1789198
Georgi Kalchev	1855441
Ania Bărbulescu	1823612

AI-powered app intended to assist tourists in cities

Introduction

Problem statement

Tourism is a vital component of many cities' economies, attracting millions of visitors each year. However, tourists often face challenges when navigating unfamiliar cities, such as language barriers, difficulty finding attractions, and accessing real-time, reliable information about transportation, local events, and cultural landmarks. Traditional resources, like guidebooks, maps, static information kiosks, or local transportation apps, often fall short in providing personalized, interactive, and immediate assistance. Human guides, while helpful, may not always be available, accessible, or financially sustainable. As tourism continues to expand globally, there is a pressing need for an innovative, app-based solution (the "social robot") that enhances the tourist experience. By leveraging technology, this solution can provide real-time, personalized, and interactive assistance, addressing key pain points for travelers while improving accessibility and convenience.

A significant challenge in digital information retrieval for travelers is the linguistic bias present in search engines and AI-driven platforms. Studies have shown that these systems tend to prioritize content in dominant languages—particularly English—over regionally relevant insights. This bias can make it difficult for tourists to discover authentic, locally cherished destinations, cultural nuances, and lesser-known experiences that may not be well-documented in globally dominant languages. Even when travelers attempt to search in local languages, AI models and search engines often rely on translations or summaries based on English-language sources, reinforcing a homogenized, commercialized view of a destination. Addressing this issue requires a technology-driven solution that prioritizes diverse linguistic sources and provides tourists with culturally embedded, real-time recommendations.

While this problem statement is broad at the outset, it serves as a starting point for our research. Through market analysis and feasibility studies, we will refine the scope to address the most pressing challenges faced by tourists. Starting with a broad problem statement allows us to explore various pain points and potential solutions before honing in on a specific, impactful approach. This iterative refinement process ensures that our final solution is both viable and effectively meets the needs of travelers.

Objectives

The primary objective of this project is to develop an AI-powered app designed to assist tourists in cities by providing real-time, interactive, and personalized support. The robot will serve as a multilingual city guide, offering recommendations, directions, cultural insights, and event updates to tourists in a friendly and accessible manner. Our key objectives are to:

• Enhance the tourist experience: Provide immediate, accurate, and useful information to visitors.
• Promoting local attractions and businesses: Offer tailored suggestions to tourists based on the tourists’ interests and the interests of the local community
• Establish an effective means of communication: The social robot should establish an effective means of communication with the tourists so that the social robot can be understood effectively

Target group and users

The primary target group for this AI-powered app consists of domestic and international tourists exploring cities. Tourists often arrive in unfamiliar environments with limited knowledge of the city's layout, attractions, and cultural norms. Language barriers, confusing public transportation systems, and the overwhelming amount of information available online can create a sense of disorientation and frustration. The social robot is designed to address these challenges by serving as an intelligent, interactive guide that enhances the tourist experience from the moment they arrive until their departure.

A significant secondary target group, and the group poised to benefit most from a financial perspective, are local business owners and the city tourism board in the areas where the social robot operates. By enhancing the overall experience for tourists, these stakeholders can directly influence visitor satisfaction, which, in turn, contributes to long-term economic growth within the city.

The social robot, through its personalized recommendations and real-time information delivery, can promote local attractions, restaurants, shops, and cultural events. This not only increases tourist engagement with local businesses but also fosters greater spending and prolonged stays. By providing tailored suggestions based on user preferences, the robot acts as a dynamic marketing tool, directing tourists toward hidden gems, seasonal events, and locally-owned establishments that might otherwise go unnoticed.

For the city tourism board, the robot serves as a powerful tool for promoting the city’s cultural identity and ensuring positive word-of-mouth among visitors. A seamless, enjoyable tourist experience enhances the city’s reputation, encouraging repeat visits and attracting new travelers.

State-of-the-art

Number	Summary	Citation
1	Mobile application with p.o.i. and ML to guide tourists between p.o.i.	T. Ghani, N. Jahan, S. H. Ridoy, A. T. Khan, S. Khan and M. M. Khan, "Amar Bangladesh - a Machine Learning Based Smart Tourist Guidance System," 2018 2nd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), Kolkata, India, 2018, pp. 1-5, doi: 10.1109/IEMENTECH.2018.8465377. keywords: {Smart phones;Mobile applications;Databases;Google;Machine learning;Real-time systems;Linear regression;smartphone;mobile application;technology;machine learning;analysis},
2	Using courd-sourced movement data tourists are guided based on how other tourists went	Basiri, A., Amirian, P., Winstanley, A. et al. Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. J Ambient Intell Human Comput 9, 413–427 (2018). https://doi.org/10.1007/s12652-017-0550-0
3	Old system first introducing a tourist guide, without the use of mobile data or GPS	Cheverst, K., Davies, N., Mitchell, K., & Friday, A. (2000). Experiences of developing and deploying a context-aware tourist guide. Proceedings Of The 28th Annual International Conference On Mobile Computing And Networking. https://doi.org/10.1145/345910.345916
4	the a long and windy introduction to the use of API's	El-Sofany, H., & El-Seoud, S. A. (2011). Mobile Tourist Guide â?? An Intelligent Wireless System to Improve Tourism, using Semantic Web. International Journal Of Interactive Mobile Technologies (iJIM), 5(4), 4. https://doi.org/10.3991/ijim.v5i4.1695
5	development of a computer simulator that can evaluate the effectiveness of various evacuation guidance methods for tourists from disaster areas	Emori, N., Izumi, T., & Nakatani, Y. (2016). A support system for developing tourist evacuation guidance. In Springer eBooks (pp. 15–28). https://doi.org/10.1007/978-981-10-0551-0_2
6	Mobile application with p.o.i.	Chavan, R., Bhoir, M., Sapkale, G., & Mhatre, A. (2023). Smart Tourist Guide System. Engpaper Journal.
7	Findings reveal that the guides do not believe that digital development will affect their jobs negatively soon but they know there is a threat of robots with artificial intelligence, the use of digital applications, and smart technologies.	Nazli, M. (2020). THE FUTURE OF TOURIST GUIDANCE CONCERNING THE DIGITAL TECHNOLOGY: a COMPARATIVE STUDY. International Journal of Contemporary Tourism Research, 66–78. https://doi.org/10.30625/ijctr.692463
8	Then by defining collaborative filtering approach on the history meaningful POIs are extracted.	Fenza, G., Fischetti, E., Furno, D., & Loia, V. (2011). A hybrid context aware system for tourist guidance based on collaborative filtering. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzzy.2011.6007604
9	The paper also describes our development of an efficient broadcast mechanism which enables visitors' requests for information to be serviced quickly despite the wireless communications infrastructure employed.	Davies, N., Cheverst, K., Mitchell, K., & Friday, A. (1999). “Caches in the air”: disseminating tourist information in the GUIDE system. Proceedings WMCSA’99, 11–19. https://doi.org/10.1109/mcsa.1999.749273
10	This paper is a historical analisis of where the tourist guide comes from	Cohen, E. (1985). The tourist guide. Annals of Tourism Research, 12(1), 5–29. https://doi.org/10.1016/0160-7383(85)90037-4
11	This paper describes mobile tourist guide - a complex system that enables comprehensive up-to date information search along with personalized recommendations and services.	Smirnov, A., Kashevnik, A., Balandin, S. I., & Laizane, S. (2013). Intelligent Mobile Tourist Guide. In Lecture notes in computer science (pp. 94–106). https://doi.org/10.1007/978-3-642-40316-3_9
12	This paper automatically generates p.o.i. by looking at what people are saying on social media	Meehan, K., Lunney, T., Curran, K., & McCaughey, A. (2016). Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system. Journal of Hospitality and Tourism Technology, 7(3), 281–299. https://doi.org/10.1108/jhtt-10-2014-0064
13	Our proposed system is a centralized system based on web services which provides all necessary information and tools that can be used by tourists to organize their trip.	Singh, V., Bali, A., Adhikthikar, A., & Chandra, R. (2014). Web and mobile based tourist travel guide system for fiji’s tourism industry. Asia-Pacific World Congress on Computer Science and Engineering, 1–7. https://doi.org/10.1109/apwccse.2014.7053840
14	proposes an alternative network to 4G specially for tour guides	Davies, N., Cheverst, K., Mitchell, K., & Efrat, A. (2001). Using and determining location in a context-sensitive tour guide. Computer, 34(8), 35–41. https://doi.org/10.1109/2.940011
15	evaluating different guideance systems to prevent overcrowding during an emergancy	Kinugasa, S., Izumi, T., & Nakatani, Y. (2012). Evaluation of a support system for large area tourist evacuation guidance: Kyoto simulation results. IEEE, 440–445. https://doi.org/10.1109/scis-isis.2012.6505119
16	The proposed model mimic the human tourism guide, through building relationships between knowledge based-system with the role of tourist-guide	Owaied, H., Farhan, H., Al-Hawamde, N., & Al-Okialy, N. (2011). A model for intelligent tourism guide system. Journal of Applied Sciences, 11(2), 342–347. https://doi.org/10.3923/jas.2011.342.347
17	Explores the pitfalls in a tourist guide system, mainly overcourding	Bornt, Christian & Cheverst, Keith. (2003). Social and technical pitfalls designing a tourist guide system.
18	Google maps with custom p.o.i.	Soe, H., & Sein, M. M. (2017). Tourist Guide Information System using Google Map and GPS. International Journal of Advanced Engineering Research and Science, 4(3), 205–209. https://doi.org/10.22161/ijaers.4.3.32
19	Overall, the feedback emphasizes the need for design improvements to enhance the tourist experience and competitiveness of Yuanjiacun.	Zhang, X. ., Disatapundhu, S. ., & Waijittragum, P. . (2024). AN EXAMINATION OF VISUAL GUIDANCE SYSTEMS FOR TOURIST ATTRACTIONS: CASE STUDY OF YUANJIACUN SCENIC AREA. FOCUS ON ARTS : FAR, SSRU, 2(2), 21–33. retrieved from https://so18.tci-thaijo.org/index.php/forfar/article/view/803
20	A guidance routing system based on GPS, the multi-routes are pre-calculated	US20060100778A1
21	Glasses to capture what the user is looking at for guidance	US10268888B2
22	AR on streetview to show POI	US11692842B2
23	A humanoid looking system to guide users	US11409294B2
24	A guidance system to help tourist using POI on Google Maps	Hema, L., Indumathi, R., Prabhakaran, N., & Kumari, D. (2021). Handheld tourist guidance system using GPS. Materials Today Proceedings, 47, 351–354. https://doi.org/10.1016/j.matpr.2021.04.561
25	Introduction of an E-tourism guide	Smirnov, A., Kashevnik, A., Ponomarev, A., Shchekotov, M., & Kulakov, K. (2015). Application for e-Tourism: Intelligent Mobile Tourist Guide. e-Tourism, 40–45. https://doi.org/10.1109/iiai-aai.2015.190

State of the art of AI trip planners

AI-powered trip planners are transforming travel by providing automated itineraries, personalized recommendations, and time-structured plans. To test their effectiveness, we used the same prompt across multiple AI trip planners: a 3-day trip to Amsterdam (March 7-9) with friends, focused on art and museums. The results showed that while these platforms are useful for general planning, they often lack localized insights, real-time event integration, and travel logistics considerations. For the images of the apps, follow this link: https://docs.google.com/document/d/10K9Qo3mwOFIJjQCA1PH3obCA7fBsB9op1oJJBdDsyHQ/edit?usp=sharing

1. Layla AI Trip Planner

Layla offers both free and paid versions. The free version gives a brief itinerary featuring must-see attractions, an estimated cost, and a hotel suggestion. While easy to use, its recommendations remain broad and lack hidden cultural gems or unique local experiences.

2. AI Trip Planner (BuildAI.space)

This tool provides a detailed itinerary with major attractions, dining spots, and a structured time plan. However, it fails to consider travel times, costs, and local recommendations, making it difficult for travelers to efficiently explore the city.

3. Mindtrip AI

Mindtrip offers a day-by-day plan with a built-in map, helping users visualize their trip. However, the recommendations focus only on the most popular tourist spots, with no emphasis on local or niche attractions.

4. Wonderplan

Wonderplan provides a general overview of Amsterdam and a list of the 26 most popular hotels, but lacks an actual structured itinerary. This makes it less useful for travelers seeking a planned cultural experience.

5. Tripadvisor AI Planner

Tripadvisor offers one of the most advanced AI trip planners, allowing users to customize their journey based on preferences, such as pet-friendly travel. However, it fails to account for travel distances between attractions, making its recommendations impractical for efficient city exploration.

6. Plantrip.io

A Dutch-based planner, Plantrip.io structures trips well and provides time-optimized plans. However, its suggestions remain broad, without offering local insights or real-time updates on cultural events.

Questions for primary research for defining the problem

1. Public transport

a. Prices and cards (weekend card, week card, 48 hours so on)

b. Navigation of public transport

• Is often transportation a common necessity in the place you like to visit?
• Do you have difficulties finding appropriate (fit within your budged, plans, etc) transportation as a tourist?
• How do you usually deal with the issue of transportation while visiting?  Local taxi, uber, walking, public transport, rented bikes, rented scooters, rented car, etc?
• Where do you find the information on the local transportation?
• Do you often need/found it useful to get a card for public transport (weekend card, week card, etc)? Specify in which situations and which kind of card if applicable.

2. Routes for visiting multiple attractions

a. Routing might be too much for a use course – out of the scope

3. Currency information

• If applicable, where do you do you find information on the currency of place to visit?
• How/ Where do you go to make exchanges of currency, before or after reaching your destination?
• Do you have issues finding such information?

4. Food ideas

a. Find the right place for someone

i. Ask for preferences – food preferences, budget, local food

• Do you prefer to eat local cuisine or more international cuisine as a tourist?
• Do you often have difficulties finding local spots that fit your food preferences and/or restrictions?
• If applicable, how do you find places to eat that fit your specific requirements?
• Do you plan the food budged in advance? How?
• Is there any tool you find useful to find places to fit into the food budged?

ii. Local food summaries

• If applicable, where do you get information on the local cuisine?
• Would you find a summary of local traditional cuisine and restaurants useful as a tourist?

iii. Avoid tourist places and find actual good local food

• Would you want to find local spots to eat instead of more tourists restaurants?
• If applicable, how do you find good local spots and attempt to avoid tourist restaurants?

b. Query google and take the data from there

• What software do you use to find placed to eat as a tourist? (Google, Yelp, TripAdvisor, etc)?
• If applicable, do you find these software useful for finding more traditional local restaurants?

5. Find good not that popular attractions

a. How do they find local hidden gems – for food, attractions,

• Do you usually want to visit less known/popular spots?
• What kind of hidden gems do you like to visit? Food, events, museums, art installations, graffiti, etc?
• How do you find such spots? If you ask someone, who?
• If you find them on your own, explain your process of where you look, which kind of sites, blogs, magazines, travel agency, etc.
• Do you have difficulties finding such spots?

6. Find events

• Do you look for local events as part of the trip?
• How do you tend to find out about local events, before and after reaching your destination?
• Would you like to attend more international events or more local ones? Take into account possible language barriers you have to deal with.
• Do you have difficulties finding local events?

7. Budget planning

• If applicable, do you use any technology to plan your trip’s budget?
• If yes what do you use and why?
• If not, are there any common things that can often make budgeting complicated?
• Would you find it useful to have a software to suggest a plan for your trip based on your budged? Why yes or not?

8. Schedule maker

• Have you ever used a schedule maker before?
• Do you find the option to automatically have your visiting schedule suggested for you useful?
• Does a higher level of personalization of the schedule based on your indicated interests make any changes in your opinion?

9. App design

• Are there any features that caused you issues or you found annoying in the travel apps that currently exist? If yes, mention the app and explain the issue with its feature.
• Are there any features that were very useful to you in the travel apps that currently exist? If yes, mention the app and explain the how its feature was useful.
• Are there any features that you haven't (fully) found in existing travel apps but you thought would be very useful? If yes, explain the feature and what it would solve.

Overall questions

• Is there some place where you get most of your information when planning?
• Do you tend to make a plan in advance or just figure things out on the go?
• If on the go, how and what do you use to figure out what to do while visiting?
• Have you ever used AI (ChatGPT, etc) to aid in your planning? If yes, how did you find the experience? Any complications?

User study

Survey results

The wiki experienced issues displaying images. You can find the results here: https://drive.google.com/file/d/1jHhfxTPQImgIav6mnnbPl-XDslybWFmp/view?usp=sharing

A more thorough summary can be found here: File:Summary Questionnaire.pdf

Key Findings from the User Study

How Travelers Search for Information

The study revealed that most users rely on Google Search, TripAdvisor, and YouTube when planning their trips. Some also turn to AI tools like ChatGPT, particularly for gathering initial ideas and general research. However, users emphasized that while AI is useful for broad overviews, it is unreliable for specifics such as hotels, restaurants, and real-time travel details. The concern stems from outdated information and hallucinated (made-up) recommendations, which make AI-generated content less trustworthy for detailed planning.

Given this, our app will use AI primarily for overviews rather than for generating specific recommendations. To ensure accuracy, we will integrate the Google Search API to provide up-to-date food-related results, while AI will structure and refine this information for better readability.

Challenges Due to Language Barriers

One of the biggest difficulties travellers face is the inability to search in the local language of their destination. This limitation often restricts the accuracy and scope of the information they can find. Several destinations were identified as particularly difficult in terms of information accessibility, including Malaysia, Eastern Europe (Serbia, Romania, etc.), France, Japan, and Georgia. In places like France, much of the relevant travel content is only available in French, and in Japan, users reported challenges in understanding directions and finding reliable sources in English. In many cases, travellers either relied on Google Translate, luck, or asking locals to navigate these challenges.

Low Demand for Certain Travel Assistance Features

The study also indicated that transportation, currency exchange, event discovery, and budgeting are not major concerns for most travellers. Users generally do not struggle with finding public transport options, as they rely on Google Maps, local apps, or transportation websites. Similarly, budgeting tools were not seen as necessary, as travellers either manage expenses on their own or prefer not to be restricted by automated budgeting tools. Most participants also expressed a preference for spontaneity over structured, AI-generated trip plans or itineraries, indicating that automated planning features would not align with their travel habits.

Given this, our app will not include features related to public transportation, budgeting, event discovery, or planning as users prefer handling these aspects independently.

Finding Local Food: A Key Challenge

One of the most valuable insights from the study is that travellers actively seek summaries of local cuisine when visiting a new destination. However, finding authentic local food spots is not always straightforward. Users currently rely on a mix of methods, including Google and social media platforms (Instagram, YouTube, and Pinterest), recommendations from locals (friends, accommodation staff), and reading restaurant reviews in the local language. Many travellers also avoid tourist-heavy restaurants by looking for menus in the native language, reviews written by locals, and restaurants with mostly local customers.

Given these findings, our app will focus on food-related travel guidance by combining Google Search API with AI-generated summaries. This will allow users to access structured lists of local restaurants, information about traditional dishes, and food recommendations that overcome language barriers.

Interest in Hidden Gems and Less Touristy Spots

Many travellers expressed a strong interest in discovering hidden gems, including local restaurants, scenic viewpoints, lesser-known cultural sites, and unique events. To find these places, users often turn to Reddit (e.g., r/[destination]), Atlas Obscura, Google Maps, or local recommendations. Some travellers rely on exploring a city on foot, while others prefer reading travel blogs and niche forums.

While the app will initially focus on local food recommendations and cuisine summaries, it will be designed with flexibility in mind, allowing for future expansion based on demand. By leveraging the same methods used to identify authentic dining experiences, the app could be extended to help users discover local hidden gems such as scenic viewpoints, cultural sites, and unique attractions. This approach ensures that, if needed, the platform can seamlessly incorporate broader travel insights while maintaining its core functionality and reliability.

Language bias in search engines and LLMs

Search engines and AI-driven information retrieval systems are often perceived as neutral tools for accessing knowledge. However, a growing body of research suggests otherwise, revealing significant biases that favor dominant languages—particularly English—and culturally influential communities. This linguistic bias has profound implications, particularly for travelers seeking authentic local experiences. The difficulty in uncovering lesser-known, locally cherished destinations stems from search engines prioritizing globally dominant narratives over regionally significant information.

Luo, Puett, and Smith (2024), in their study A 'Perspectival' Mirror of the Elephant, investigate how search engines and AI-driven platforms such as Google, ChatGPT, YouTube, and Wikipedia reflect linguistic and cultural biases. They find that English-language searches overwhelmingly return content shaped by dominant Western perspectives, whereas searches conducted in local languages yield more culturally embedded information. Their research highlights how search algorithms reinforce a globalized knowledge hierarchy, making it harder for users—such as travelers seeking authentic local experiences—to discover region-specific insights. This supports the idea that tourists relying on English-language searches are more likely to be directed to mainstream, commercialized venues rather than authentic local spots.

Similarly, Rovira, Codina, and Lopezosa (2021), in their study Language Bias in the Google Scholar Ranking Algorithm, provide evidence of the systemic preference for English-language academic content. Their research reveals that Google Scholar ranks English-language papers significantly higher than those written in other languages, making non-English research nearly invisible in search results. This has broader implications beyond academia, as it reflects a general pattern in which English content is prioritized across various digital platforms. For travelers, this means that searches conducted in English will primarily surface popular, widely reviewed locations rather than smaller, locally favored spots that are discussed primarily in the local language.

The broader issue of search engine bias is extensively explored by Segev (2010) in Google and the Digital Divide: The Bias of Online Knowledge. Segev argues that Google systematically amplifies the knowledge and perspectives of dominant linguistic communities, creating a "digital divide" in which non-English content is suppressed. This digital divide has real-world consequences for those seeking culturally specific or community-driven insights, as it means that valuable local knowledge—such as recommendations for authentic dining experiences or hidden historical sites—remains largely inaccessible unless users explicitly search in the local language.

The problem extends beyond search engines to AI-powered tools, as highlighted by Sharma, Murray, and Xiao (2024) in their study Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models. Their research finds that large language models (LLMs) such as ChatGPT exhibit significant biases towards high-resource languages like English. When queried in a low-resource language, these models often provide translations or summaries based on English-language sources rather than drawing directly from native-language content. This creates a misleading sense of linguistic inclusivity while reinforcing the dominance of English-language information. For travelers relying on AI-driven recommendations, this means that even when searching in a local language, the results may still reflect globally dominant perspectives rather than authentic local insights.

The combined findings of these studies illustrate a crucial limitation of mainstream search engines and AI-driven information retrieval systems: their structural preference for English over other languages. This bias disproportionately affects travelers seeking authentic experiences, as it obscures regionally specific knowledge in favor of more globally recognized content. Addressing this issue requires a shift in search strategies—such as conducting searches in the local language—to uncover the true essence of a destination as experienced by its residents.

References

Sharma, N., Murray, K., & Xiao, Z. (2024). Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models. arXiv preprint arXiv:2407.05502.

Rovira, C., Codina, L., & Lopezosa, C. (2021). Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 13(2), 31. https://doi.org/10.3390/fi13020031

Luo, Q., Puett, M. J., & Smith, M. D. (2024). A" perspectival" mirror of the elephant: Investigating language bias on google, chatgpt, youtube, and wikipedia. Queue, 22(1), 23-47.

Segev, E. (2010). Google and the digital divide: The bias of online knowledge. Elsevier.

Examples Local Languages

Example in different answers with ChatGPT

https://chatgpt.com/share/67ceb278-9e64-8001-89c3-8c69b17c250b (dutch)

https://chatgpt.com/share/67ceb349-ce94-8001-9293-1c8ebee18c64 (english)

Example in different answers with Google

What to do in Oosterwolde (Netherlands) for 3 days

https://drive.google.com/file/d/1opk7nSgiaK9JOGaqzHFpwYB-Iho97q0K/view?usp=sharing (english)

https://drive.google.com/file/d/1uwGIkJMHQ-Lg3j_3UDulLRahKTdiB8x9/view?usp=sharing (dutch)

What to do in Amsterdam (Netherlands) for 3 days

https://drive.google.com/file/d/1ge3xhAsADhl--j1JkVXXxire1eYKB0va/view?usp=sharing (english)

https://drive.google.com/file/d/1sUna-asTTEvadavNUhyG86Qc2NthQtGp/view?usp=sharing (dutch)

Restaurant in Leeuwarden

https://drive.google.com/file/d/1aGepMA9AmffRSKTzRtK41bEoX_E-z9Rc/view?usp=sharing (dutch)

https://drive.google.com/file/d/1p922s1LeKWPuC7kr59TURoCqDzF_Dwid/view?usp=sharing (Frysk - dutch dialect)

Dialects and food

Language plays a crucial role in shaping cultural identity, and its influence extends deeply into local food traditions. The linguistic diversity found in various regions across the world reflects a society’s history, traditions, and culinary heritage. The relationship between language and food is evident in how local dishes are named, described, and preserved through generations. Dialects, in particular, play a significant role in maintaining the authenticity and uniqueness of regional cuisines.

The Role of Language in Culinary Traditions

Language serves as a bridge between food and culture, helping communities articulate their culinary practices and pass them down through generations. Research has shown that incorporating local dishes into language education can strengthen cultural roots and facilitate the dissemination of traditional knowledge (Tyas, 2017). For example, in Indonesia, adapting locally recognized culinary terms in learning materials helps introduce traditional dishes more effectively (Ainun et al., 2024). Similarly, ecolinguistic studies emphasize how language embodies cultural identity, as seen in the lexicons related to Balinese flora-based food processing (Umiyati & Pratama, 2021). The integration of food-related vocabulary in language learning highlights the deep connection between language and local cuisine, ensuring the survival of culinary heritage.

The Impact of Dialects on Local Food Terminology

Dialects contribute significantly to the linguistic representation of local food, influencing how dishes are named and described within specific communities. Studies on Vologda dialects in Russia illustrate how food traditions are embedded in speech genres such as dining conversations, memoirs, and culinary recipes (Zhandarova & Kryuchkova, 2023). Similarly, research on Sundanese dialects in Indonesia reveals lexical variations in food naming, encompassing changes in sounds, word formation, and semantics (Durahman & Badriah, 2022). These studies demonstrate that dialects not only reflect regional linguistic diversity but also serve as repositories of traditional culinary knowledge.

One of our team members, who is from Friesland, also discovered notable differences when searching for food-related information in the national language versus the local dialect spoken in Friesland. Some restaurants had distinct names in the dialect. This highlights how dialects influence access to and awareness of regional cuisine, reinforcing the importance of preserving linguistic diversity in culinary contexts. This example can be found by "Examples local languages".

In many cases, the specific dialectal terms for food ingredients and preparation methods are unique to a region and may not have direct translations in standard languages. This uniqueness underscores the importance of preserving dialects to maintain the integrity of local food traditions. However, the decline of dialect usage in some communities poses a challenge, as younger generations may lose access to the rich culinary lexicons of their ancestors (Zurriyati & Sinar, 2018). This shift threatens the continuity of traditional food knowledge, emphasizing the need for conscious efforts to document and promote dialectal culinary expressions.

The Cultural and Economic Implications

The interplay between language and local food extends beyond cultural identity to economic and tourism aspects. Many regions capitalize on their unique food culture to attract visitors, and the linguistic representation of local cuisine plays a key role in marketing traditional dishes. For instance, the use of indigenous terms for dishes in menus and food festivals enhances authenticity and fosters a deeper appreciation of regional heritage. In multilingual societies such as Cameroon and Indonesia, the coexistence of multiple languages and dialects allows for rich culinary exchanges while preserving local identities (Awah, 2021; Muth’im & Sutiono, 2024).

Moreover, linguistic policies that support multilingualism can help sustain the visibility of local food traditions. Countries like Belgium, Switzerland, and South Africa have implemented language policies that promote linguistic diversity, which can indirectly benefit the preservation of culinary heritage (Deprez & Plessis, 2000). These policies ensure that regional dialects and languages continue to be valued, allowing food-related terminologies to thrive alongside mainstream languages.

Bibliography

Ainun, A., Zohriyah, A. M., Sholikhah, U. N., Isnaini, H. N., & Apriyanto, F. R. (2024). JAVANESE CULINARY GLOCALIZATION THROUGH LEARNING INDONESIAN. Proceeding Of International Conference Cultures & Languages., 2(1), 563–583. https://doi.org/10.22515/iccl.v2i1.9673

Awah, P. K. (2021). Multilingualism in Cameroon: An Expression of Many Countries in One Country. In IntechOpen eBooks. https://doi.org/10.5772/intechopen.99703

Deprez, K., & Plessis, T. (2000). Multilingualism and government : Belgium, Luxembourg, Switzerland, former Yugoslavia, South Africa. https://www.semanticscholar.org/paper/Multilingualism-and-government-%3A-Belgium%2C-former-Deprez-Plessis/a7a8abfb601bc516803d04cc75070a4807aad566

Muth’im, A., & Sutiono, C. (2024). Maintaining Multilingualism in a Multi Culture Country: The Case of Indonesia. Arab World English Journal For Translation And Literary Studies, 8(1), 184–194. https://doi.org/10.24093/awejtls/vol8no1.14

Tyas, A. S. P. (2017). Identifikasi Kuliner Lokal Indonesia dalam Pembelajaran Bahasa Inggris. Jurnal Pariwisata Terapan, 1(2), 38. https://doi.org/10.22146/jpt.24970

Umiyati, M., & Pratama, N. A. D. Y. (2021). Flora Ecolexicon and Procedural Eco-Text of Processing Bali Local Culinary. RETORIKA Jurnal Ilmu Bahasa, 7(2), 106–114. https://doi.org/10.22225/jr.7.2.3955.106-114

Van Herck, R., & Vermandere, D. (2016). When the language bonus becomes an onus: a Belgian case study. https://www.semanticscholar.org/paper/When-the-language-bonus-becomes-an-onus%3A-a-Belgian-Herck-Vermandere/2b6da3e4f4d7082df8bebb027a2ebb81cecda739

Zurriyati, Z., & Sinar, T. S. (2018). THE POWER OF CULINARY IN SUSTAINING ACEH ECOLEXICON. AICLL ANNUAL INTERNATIONAL CONFERENCE ON LANGUAGE AND LITERATURE, 1(1), 75–80. https://doi.org/10.30743/aicll.v1i1.11

App UI Design

When designing an application, the user is the most important factor. The simplest of ways to taking a general user into account in the design process is by considering psychological principles that can affect the ways users experience the app and its functionalities.

Thus, we will continue by outlining important factors based on psychological finding and how they translate in terms of UI design.

1.    Gestalt laws[1][2]

The principles of Gestalt outline how people tend to organize the results of their perception. Knowing the ways the average user perceives the content of a screen can be used to design a UI that results in an easier navigation of the app and a more visually appealing interface.

These laws identify the following :

·      Symmetry is seen as more organized.

·      The mind has the tendency to fill in incomplete information or objects.

·      Simple design are good. Simplicity aids in faster understanding of information.

·      Elements can be grouped toghether by having a visual connection (same color, same shape, move in the same direction, close position) or a similar arrangement of the elements that creates continuity.

·      Similar elements are better grouped to attract the attention of the user.

·      Each screen should have a focal point.

·      Different colores can be used to define areas that are perceived as separate objects.

·      Complex objects tend to be interpreted in the more simple manner.

2.     Information processing

According to the study by George A. Miller, on average people can remember 7± 2 objects at a time. It was also found that by using certain techniques to aid memorization this limit can be surpassed, but this does not nullify the effect of Miller’s study as UI design should be made as simple to interact with as possible. [3] This implies that each screen of an app should be designed to only have 7±2 elements so users have less to remember and an easier time visually navigating the screen.  When it comes to the use of text and images, the “Left-to-Right” theory points that it is more convenient for users to have the most important information on the top left side of the screen. Furthermore, people have 2 visual fields, the right field is responsible for the interpretation of images and the left for text. Putting images on the left and the text on the right would makes it easier for users to process the given information. [2] Further limitations on how people process information is tied to the limitations of motor systems. Multitasking of a motor system or motor systems should be avoided. Focusing on having a task of one motor system introduced at a time makes it easier for users to process the information and to have an easier time paying attention to the task. Thus, screens should not give the users multiple information of different types at the same time. [2]

3.     Use of Colors

Colors can be used to improve the visual appeal of an app. The only issue is that the color selection is a complex subject. Color schemes can be chosen based on the context they are to be used, different color theories and the desired effect on the user. Nevertheless, it is important that colors are used consistently across the app, and they are not overused.[1] Regarding accessibility, the limited color perception of color-blind people should be considered when choosing contracting colors to highlight different elements. [1] It should also be noted that bright and vivid colors, or a mix of bright and dark colors can tire the eye muscles. This should be avoided to make the app easier to use for longer periods of time. [2]

4.     Feedback

Feedback is quite important for users to feel that their actions have an effect. [5] It can also affect the ease with which users can remember how to use an app. Feedback should be immediate, consistent, clear, concise and it should fit the context of their actions.[1]

5.     Navigation and guiding users

It can be important to make it clear to users how they should begin interacting with an interface. To make it clear it would be useful to make the starting element stand out. This can be done either through a different color, size, hue, shape, orientation etc. [5] Guiding the user can further be done through a visual hierarchy. Important elements should be made to stand out in general due to the phenomenon of inattentional blindness. The Invisible Gorilla Experiment by Simons and Chabris in 1999 showed that people will miss unexpected elements when they are focused on a different task.[4] Another aspect that helps in navigation an interface Is that the user should be able to find a logical consistency in it. The responses to user actions should be consistent and any changes from whatever monotony was created should be predictable. The responses should also be reflective of the content of the interface. [5] Further important aspect to ease navigation for users is providing them with a clear reversal or exit option. Such options give a sense of confidence to users and make navigating the app less stressful once they know that they can opt out if they make a mistake.[5]

6.     Efficiency

Hick’s law states that the more options available the longer it takes to decide. In term of UI design, this implies that menus and navigations systems should be simplifies, either there should be a focus on a few items or elements should be labeled well and similar elements should be grouped together. Another way to decrease options would be to create a visual hierarchy. [1] Fitts Law is based around the connection between target size and distance, and the times it takes to reach a target. When it comes to UI, the law implied that bigger buttons are in general faster to use and better suited for the most frequently used elements. It can reinforce that the steps of a tasks should be contained on the same screen to make navigation more efficient. [1]

Bibliography

1. Anik, K., Shahriar, R., Khan, N., & Omi, K. S. I. (03 2023). Elevating Software and Web Interaction to New Heights: Applying Formal HCI Principles for Maximum Usability. doi:10.13140/RG.2.2.14304.76803/1

2. Yee, C., Ling, C., Yee, W., & Zainon, W. M. N. (01 2012). GUI design based on cognitive psychology: Theoretical, empirical and practical approaches. 2, 836–841.

3. Miller, G. (04 1994). The Magical Number Seven, Plus or Minus Two: Some Limits on Out Capacity for Processing Information. Psychological Review, 101, 343–352. doi:10.1037/0033-295X.101.2.343

4. Drew, T., Võ, M., & Wolfe, J. (07 2013). The Invisible Gorilla Strikes Again Sustained Inattentional Blindness in Expert Observers. Psychological Science, 24. doi:10.1177/0956797613479386

5. Blair-Early, A., & Zender, M. (2008). User Interface Design Principles for Interaction Design. Design Issues, 24(3), 85–107. http://www.jstor.org/stable/25224185

UI Design Devevelopment

Based on our research into general guiding rules and into the design of the state-of-the-art, we started working on a UI design in Canva. This design can be found athttps://www.canva.com/design/DAGhr-Hba-g/9xXWZd0l48p5EB3g95nlXA/edit?utm_content=DAGhr-Hba-g&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton.

At the same link we were working on the logo for the app, which is still in development. Multiple variations have been made with the aim to do some primary research for the feedback of our target user base. The logo was inspired by a compass depiction and the northern stars, both deeply intertwined with the concept of navigation and finding your way in multiple cultures. After deciding on StarPlate as the name of our app, we decided to incorporate a plate in the logos as well to create a cohesive, clear and memorable branding.

The design is being made with a softer color scheme, with a stronger shade of blue as its defining color similar to how green is used by TripAdvisor and other travel apps. Blue was chosen for its calming effect and its associations with the sky and open seas, both tied to elegance and a sense of freedom and of exploration. We are also aiming to lean into the idea of tradition, local and beauty to guide the font and the pastel tones of secondary colors in our chosen palette.

As a user enters the app, users can log in or continues as guests and use the app directly. Having an account would give users the ability to bookmark interesting restaurants and foods presented. After moving on from the front screen, users an introductory screen if they are a guest or login for the first time. From there can press either on the introduction screen or the navigation bar at the bottom on a button to change to the searching screen. On the searching screen users have to select the country and region/city they are interested in and then they can choose to view an overview of traditional local foods. From the overview of traditional local foods, users can select a dish they want to try and get a selection of restaurants that sell them. Logged in users can also bookmark dishes. On the search screen users can also select some filters for what kinds of restaurant they are looking for based on distance, price range and star rating. As users get a list of recommended restaurants based on their filters with minimal information of each of them. For further info users can click on the restaurant card from the list and get more information and the location. Logged in users can also bookmark restaurants from each restaurant's individual page.

App Backend Design

Overview

The food recommendation app will be developed using Android Studio, with Java as the primary programming language. For the initial implementation, API keys will be hardcoded into the app.

Core Functionality

The app's functionality revolves around gathering keywords related to food searches, which can be done in two ways:

1. Using an LLM API to generate relevant search terms.
2. Using a predefined lookup list based on location.

Once the keywords are identified, the app will query the Google Search API to fetch relevant food-related results. These results will then be used to generate a structured prompt for an LLM, ensuring the response follows a proper JSON format.

Structured Prompt Format

The app will prompt the LLM using the following JSON structure:

Please only respond in JSON using the following format:

{

  "dish": "example_dish",

  "ingredients": ["ingredient_1", "ingredient_2"],

  "best_places_to_try": ["restaurant_1", "restaurant_2"],

  "cooking_tips": ["tip_1", "tip_2"]

}

Make sure your response is in English.

You are a food assistant providing structured culinary recommendations.

Based on our Google search results in the local language, we found: {Google search results here}.

Note: The details and format of the prompt will be updated throughout the development of the app to ensure the LLM provide us with useful and well-formatted output.

Response Validation

Once the LLM generates a response, the app will:

• Check if the response adheres to the expected JSON format.
• If the format is incorrect, the app will either rerun the query or send a follow-up request explicitly instructing the LLM to adhere to the format.

Displaying Results

After validating the structured response, the food/restaurant recommendations and all relevant information will be displayed in the app for the user.

Diagrams

The following diagrams describe the structure and the sequence of actions in our backend system.

First, we have a Class Diagram, which illustrates the key components, their attributes, and relationships within the backend architecture.

Class diagram for backend

Next, we have a Sequence Diagram, which outlines the flow of interactions between different components, showing how data and requests move through the system over time.

Sequence Diagram

These diagrams provide a clear visualization of how our backend is designed and how it operates dynamically.

Planning

The planning for this project is going to be done in a way that is less strict than a traditional way of project planning. We think this is possible due to the short time period in which this project is done, and because the dependence of one task on another in the later stages of the project are rather minimal. To facilitate this process we will use a project management tool called Trello. Trello’s Kanban-style board system allows us to organize tasks into lists, track progress in real-time, and easily adapt to changes as needed. Each task will be categorized based on its status (e.g., "To Do," "In Progress," "Completed"), ensuring transparency and clear accountability within the team.