Quizlet: Your New Favorite Way to Study?

8

October

2024

5/5 (2)

If you’re anything like me, you probably spend more time figuring out how to optimize your study routine than actually studying. Recently, AI powered tools have made this process even more interesting. We now have tools for taking notes (Notion AI), transcription (Otter.ai), math (Photomath), research (Consensus), and study planning (ChatGPT), with more emerging every day (Chaney, 2024; Sim, 2024). One such tool is Quizlet.

What Can Quizlet’s AI Do?

Quizlet has long been a popular option for students primarily using flashcards. Traditionally, it allowed users to create, share and study from pre-made sets while also having features like learn and quiz mode. In 2023, Quizlet introduced a range of AI powered features (Bayer, 2023). One of the additions being Magic Notes, which enables students to upload notes and automatically generate outlines, flashcards and tests.

Having used Quizlet before the introduction these AI features, I was curious to see how they would impact my study experience this time around.

Putting Quizlet’s AI to the Test

For this test I used Quizlet’s AI tools on the course Information Strategy (fittingly, the class that assigned me this blog). I tested Session 1 using my personal notes stored in Notion covering video material, lectures, readings and learning objectives. I started with the study guide generation as it seemed to offer a broad range of functionalities, allowing me to create quizzes and flashcards from the same content base. The output definitely impressed me! It took the learning objectives and generated an outline of the course content based on my notes. This included key concepts organized with headings and tables, similar to the outputs of ChatGPT. Most of the content matched my expectations with a few misinterpretations.

Next, I tested the flashcard and quiz options. For clear concepts like the definition of network effects, it performed well. However, some flashcards lacked context to be fully useful. For instance, while capturing the term “digital”, the flashcard didn’t provide sufficient detail to indicate what aspect of “digital” was being referred to. As a result, it was difficult to know which topic or definition was expected. Despite this, the interface was my personal highlight. It was well-designed and user-friendly, making it easy to switch between study modes.

Flashcard Context Comparison: Sufficient vs. Insufficient Context

Final Thoughts

Overall, using Quizlet’s new AI features was mostly a positive experience. The ease of use really stood out, especially since my notes were already digital. Additionally, the platform used a gamified design, which motivated me to continue studying. In terms of the AI-generated content, it generally met my expectations. The study guide it created from my notes was well-structured, and the flashcards worked well in some cases but required manual edits for more complex concepts.

One downside I noticed was the default setting that made study sets public. I felt constantly pushed to publish and share content. I also identified the risk of students unintentionally uploading copyrighted material. While this is prohibited by the platform, it’s still easy to overlook in practice.

In conclusion, while Quizlet’s AI features provide a strong foundation for studying, they still require some manual adjustments to be fully effective for me.

References

Bayer, L. (2023, August 8). Welcome to Quizlet’s AI Study era: Studying will never be the same | Quizlet. Quizlet. Retrieved October 8, 2024, from https://quizlet.com/blog/ai-study-era

Chaney, S. (2024, April 27). 5 AI tools for students: Use AI to help you study, summarize content, and edit papers. LaptopMag. https://www.laptopmag.com/software/5-ai-tools-for-students-use-ai-to-help-you-study-summarize-content-and-edit-papers

Sim, E. (2024, April 16). 10 best AI tools for students to learn better and faster. Study International. https://studyinternational.com/news/best-ai-tools-for-students-to-learn-better/

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Web Scraping: The Good, the Bad and the Ugly

19

September

2024

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From Innovation to Privacy Risks, and How Websites Defend Against It

What once started as an experiment to measure the true size of the internet (Norman, 2020) has long since become an integral part of it. Web scraping is not a new topic, it first emerged and gained popularity in the early 90s. But what exactly is web scraping? In short, it is the extraction of data from a website for analysis or retrieval (Zhao, 2017). The current excitement around large language models (LLMs) like OpenAI’s GPT has renewed the importance of web scraping. These models rely on massive, diverse, and current datasets to improve their performance, which can be aggregated at scale using web scraping.

But is web scraping more helpful or harmful, and what can websites do to prevent it?

The Good, the Bad and the Ugly

The Good

Web scraping can be a valuable tool for research and innovation. For instance, search engines rely on scraping to index websites and provide answers directly on search pages. Beyond this, scholars use web scraping to gather data that would otherwise be inaccessible. For example, monitoring Dark Web activity benefits fields like cybersecurity and social science (Bradley & James, 2019).

The Bad

However, scraping often disregards website terms and conditions, raising ethical and legal questions (Krotov et al., 2020). In the U.S., scraping has been challenged numerous times under laws like the Computer Fraud and Abuse Act (CFAA), with high-profile cases such as LinkedIn vs. hiQ Labs. In Europe, scraping also results in legal risks, especially when performed without consent.

The Ugly

At its worst, scraping can lead to serious breaches of privacy. Scrapers can collect sensitive data, including login credentials and personal information. Worse still, LLMs trained on scraped data may unintentionally memorize and expose this information, creating privacy concerns in AI (Al-Kaswan & Izadi, 2023).

Defending Against Scraping

To protect against web scraping, websites employ various techniques. Common defenses include requiring users to log in, implementing CAPTCHA challenges, and restricting access to private content (Turk et al., 2020). For instance, some websites require registration before allowing access to certain information, while others require the use of multi-factor authentication (MFA). This is intended to make automated logins harder. Additionally, rate limiting is used to block scrapers after a certain number of requests. Other tactics include detecting and blocking IP addresses based on blacklisting.

However, these mechanisms are not foolproof. Scrapers, which are increasingly powered by AI, can now mimic human actions such as typing delays and solving CAPTCHAs (Yu & Darling, 2019). Lastly, proxy networks are used to circumvent rate limiting and IP bans.

This back-and-forth between website hosts and scraping technologies has turned into an ongoing arms race, with AI being leveraged on both sides.

Are you a robot?

Fun fact: CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart (Google, n.d.).

References

Al-Kaswan, A., & Izadi, M. (2023). The (ab)use of Open Source Code to Train Large Language Models. https://api.semanticscholar.org/CorpusID:257219963

Bradley, A., & James, R. (2019). Web scraping using R. https://www.semanticscholar.org/paper/Web-Scraping-Using-R-Bradley-James/f5e8594d28f8425490a17e02b5697a26c5b54d03

Google. (n.d.). What is ReCAPTCHA? Google Support. Retrieved September 19, 2024, from https://support.google.com/recaptcha/?hl=en#:~:text=A%20%E2%80%9CCAPTCHA%E2%80%9D%20is%20a%20turing,users%20to%20enter%20with%20ease.

Krotov, V., Johnson, L., & Silva, L. (2020). Legality and ethics of web scraping. Communications of the Association for Information Systems, 47, 539–563. https://doi.org/10.17705/1cais.04724

Norman, J. (2020, September). Matthew Gray develops the world wide web wanderer. Is this the first web search engine? HistoryofInformation.com. Retrieved September 19, 2024, from https://historyofinformation.com/detail.php?id=1050

Turk, K., Pastrana, S., & Collier, B. (2020). A tight scrape: Methodological Approaches to cybercrime Research data collection in adversarial environments. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). https://doi.org/10.1109/eurospw51379.2020.00064

Yu, N., & Darling, K. (2019). A Low-Cost approach to crack Python CAPTCHAs using AI-Based Chosen-Plaintext attack. Applied Sciences, 9(10), 2010. https://doi.org/10.3390/app9102010

Zhao, B. (2017). Web scraping. In Springer eBooks (pp. 1–3). https://doi.org/10.1007/978-3-319-32001-4_483-1

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