Search engines

Search engines are relatively old services; for a history of search engines from 1994-2010, see PPC Blog’s History of Search infographic. Originally, search engines were set up to support information retrieval on web 1.0. However, search tools moved in a web 2.0 direction as they became more user-centred and personalised, as well as interactive. More recently, they have moved in a web 3.0 direction with the continued development of personalised search and, from 2023, the rollout of AI technology as seen in Microsoft’s Bing/Copilot (incorporating OpenAI’s GPT-4 large language model, or LLM) and Google’s Gemini (formerly Bard; incorporating Google’s own Gemini Pro/Ultra LLM); for further details, see the section on AI and future search customisation towards the end of this page. Search is of course available on mobile devices, with major search engines downloadable as apps, and hence has some links with mobile learning.

Search engines are essential to help users find materials in the vast ocean of online content. Search on both desktop/laptop computers and mobile devices is dominated by Google, with other major search engines including Yahoo! and Baidu (百度). There are also specialised search services which are worth knowing about, as these may sometimes return more appropriate results than the major search engines. In general, more customisation of search has become possible, giving users more control over search parameters as well as, in some cases, search interfaces and results display formats. Much more automated personalisation of search has also become evident, as the major search engines have worked towards ensuring that personally relevant, time-sensitive, context-sensitive information is provided to users; but serious concerns have been raised over search engines determining what is relevant and useful to individuals based on their tracking of those individuals’ browsing habits and online associations.

While there is no doubt that students need to search for information online for a variety of purposes, it has become evident that many students lack the requisite digital literacy skills in this area. This has resulted in programmes at all levels of the education system to help students acquire:

• • the search literacy to conduct effective keyword searches, which, with the shift to AI-based search, is likely to merge with the prompt literacy needed to interact effectively with AI chat interfaces
  • the information literacy and critical literacy to evaluate information and differentiate reliable from unreliable content, especially in the context of the proliferation of misinformation, disinformation and fake news across the internet in recent years
  • the tagging literacy to keep track of, categorise and organise the content found online

Key innovations in search customisation and personalisation are outlined below, along with examples.

Customisation of search content > Even prior to the advent of AI-based search, major search engines offered their own customisation tools (see for example Google Advanced Search), which has led to the disappearance of many specific search services over recent years. However, there are still a number that remain active and allow very focused searches, as seen below.

For more options, see Wikipedia’s List of Search Engines. To create your own fully customised web search, try Google’s Programmable Search Engine. To compare global search trends for different terms, check out Google Trends.

Customisation of search presentation > It is possible to choose among different presentation formats of search interfaces or search results, some of which are very different from traditional search boxes or linear lists of results. Experimentation in this area has declined in recent years, but there are still a few examples available, as seen below.

For more options, see Wikipedia’s short list of Visual Search Engines.

Customisation of search context > This is also referred to as personalisation of search (though arguably individualisation may be a better term, as it lacks the humanistic overtones of ‘personalisation’), and it is the area where we have recently seen the greatest developments. It is increasingly the case that the search context is defined in relation to the individual doing the searching, and the other people to whom that individual is connected. Search results are thus determined by algorithms which tailor the results to individuals on the basis of information tracked or inferred about them as a result of past searches, online connections, and online activities. The result is that two individuals searching for exactly the same topic using exactly the same terms in a search engine like Google may find that they receive very different results. Indeed, the ultimate aim of personalised search is seemingly to obviate the need for searching at all, with search engines becoming more like prediction engines which anticipate users’ needs. A further move in the direction of personalisation can be anticipated as AI-based search becomes more common. Interestingly, more manual contextualisation options can also be seen in the growing ability of users to provide detailed contextual information when asking questions in AI chat interfaces.

Automated personalisation also plays a major role in determining what content is seen by social media users in their feeds. In some ways, social networking, microblogging and social sharing services partly eliminate the need for searching, because relevant content is already preselected for users. As such, social media platforms may be seen as something of a threat to the dominance of search engines in accessing online content.

The key issue for educators is that when search engines or social media services tailor news and results to individual users, the range of information received becomes ever narrower. To the extent that education is about broadening rather than narrowing students’ horizons, this is a concern. For a statement about the dangers of ‘filter bubbles’, see Eli Pariser’s TED Talk Beware Online ‘Filter Bubbles’, which dates from 2011 but has become even more relevant in the intervening years.

AI and future search customisation > Further customisation and personalisation, but also further issues over tracking of individuals, are likely to accompany the ongoing rollout of general AI-powered search tools like Microsoft’s Bing and Google’s Gemini, which can be expected to rapidly gain market share. Other AI-powered search options include the Opera browser’s Aria, while ChatGPT, Perplexity AI and other AI chatbots can also serve to varying degrees as search engines. In July 2024, OpenAI announced that it was testing SearchGPT, a prototype of a new AI search feature. For more information, see the Generative AI page of this website.

Last update: August 2024.