Towards a World Wide Web without digital inequality.

The World Wide Web (WWW) empowers people in developing regions by eradicating illiteracy, supporting women, and generating economic opportunities. However, their reliance on limited bandwidth and low-end phones leaves them with a poorer browsing experience compared to privileged users across the digital divide. To evaluate the extent of this phenomenon, we sent participants to 56 cities to measure the cost of mobile data and the average page load time. We found the cost to be orders of magnitude greater, and the average page load time to be four times slower, in some locations compared to others. Analyzing how popular webpages have changed over the past years suggests that they are increasingly designed with high processing power in mind, effectively leaving the less fortunate users behind. Addressing this digital inequality through new infrastructure takes years to complete and billions of dollars to finance. A more practical solution is to make the webpages more accessible by reducing their size and optimizing their load time. To this end, we developed a solution called Lite-Web and evaluated it in the Gilgit-Baltistan province of Pakistan, demonstrating that it transforms the browsing experience of a Pakistani villager using a low-end phone to almost that of a Dubai resident using a flagship phone. A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite-Web at scale would constitute a major step toward a WWW without digital inequality.

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.