Competency H

Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.

Section 1: Introduction

Abram (2022) argues that “information professionals need to do more than just see the changes currently happening; they need to get out in front of them, forecasting what is to come and proactively moving ahead to adapt to these changes” (“Change and Adaptation in the Information Profession” section). This emphasis on anticipating technological change, rather than simply responding to it after the fact, has reframed how I think about this competency. Many of the technologies that are currently transforming how information is created and transmitted are not guaranteed to develop in ways that align with information professionals’ priorities, from open access to transparency and information literacy. If we want to shape how such technologies are adopted, we need to be, if not early adopters, at least early understanders: people who engage with emerging technologies early enough to grasp their possibilities, limitations, and implications.

Breeding (2022) grounds this in more concrete terms, describing the specific technologies about which information organizations currently must make real choices. He discusses the tradeoffs between proprietary and open source software, the shift toward cloud-based and mobile-first system design, the emergence of voice-activated technologies, and the industry’s broader move from traditional integrated library systems toward more flexible library services platforms built on modern, service-oriented architecture. What connects Breeding’s specific examples to Abram’s broader framework is that each of these represents a decision point: information organizations must be properly equipped to evaluate the pluses and minuses of specific technologies and their implementations.

Rights management provides a useful example of what this kind of evaluation looks like in practice. Coyle (2004) presciently points out that technology designed for a commercial, individual-consumer context often maps poorly onto library and institutional needs, and adapting it requires understanding how the assumptions of each context diverge. Wolfe (2013) raises a related problem in her discussion of digital asset management governance. While understanding copyright issues is crucial for many organizations, it is often impractical to expect every individual in the organization to become an expert in copyright. Instead, features like license expiration reminders and tiered access can help direct users towards good practices.

I observed technology governance problems firsthand working as a data analyst at a market research firm serving the film and television industry. Clients would instruct us to download already-released movie trailers directly from YouTube for use in surveys, until our own legal team eventually flagged this as a violation of YouTube’s terms of service and required studios to send video content to us directly instead. A proper rights-management system on the client side, something like the “lightboxes” used in digital asset management to share content externally with tracked permissions and expiration dates, could have prevented this informal workaround from becoming standard practice in the first place. In both the Coyle and Wolfe pieces and in my own experience, the technology itself is only half the problem. The more difficult, more critical work is designing the governance, training, and evaluation processes around it.

Competency H requires more than simply knowing that technologies such as DRM, large language models, virtual reality, or cryptographic hash functions exists. It requires engaging with them enough to understand their real capabilities and limitations, while also considering what their adoption would mean for a specific information environment, its users, its governance, and its risks. The evidence that follows reflects five different technologies with which I have engaged in this way.

References

Section 2: Evidence

My understanding of current and emerging technology comes from building and deploying my own portfolio site, evaluating specific digital preservation and metadata tools, working through the limits of an artificial intelligence (AI) coding tool by trying and reworking a solution to a practical problem, conducting user testing of an emerging VR platform, and ongoing engagement with current developments in AI and digital asset management.

My first piece of evidence is this e-portfolio itself, which I built using Astro, a modern static site framework, developed in VS Code, and deployed through Cloudflare Pages, with updates pushed to GitHub. Rather than using a simpler, WYSIWYG (What You See Is What You Get) platform like Google Sites, I chose this more technically demanding stack because it gave me more control over the site’s structure and styling, and because deploying my own site, debugging errors and file-path issues, and resolving CSS conflicts gave me hands-on experience with web development tools I may need to use professionally. Without previous experience, apart from tinkering with HTML in middle school, I was able to identify, use, and evaluate a current web development and hosting stack to create and deploy a personal website.

My second piece of evidence is a file preservation strategy assignment from INFO 284, in which I analyzed a collection of digital files to assess their formats, metadata, preservation risks, and appropriate preservation strategies. I used DROID, a digital preservation format-identification tool, to characterize the files and discovered a discrepancy that would not have been apparent from file extensions alone: alice_crown01.jpg had a .jpg extension, but DROID’s signature-based identification determined that the file was actually a PNG. I also examined the metadata of several image files using Adobe Bridge and compared its results with Windows 11’s native file properties. This comparison required me to interpret differences between the tools rather than simply record their output. For example, Bridge exposed technical information that was not visible in Windows, including per-channel bit depth, color profile information, and Camera Raw adjustment data, while the two applications reported different resolution values for another image.

I then applied these findings to develop a preservation strategy for the collection. I evaluated the significant properties and vulnerabilities of each file format and considered whether files should be preserved in their original formats, accompanied by preservation copies, or migrated to formats such as PDF/A, WAVE, WARC, or XSLX. I also identified situations in which migration could result in the loss of significant properties and therefore recommended retaining the original file alongside a preservation or access copy. The assignment demonstrates my ability to use digital preservation tools to characterize files, critically evaluate metadata and tool outputs, identify format-level risks, and use that evidence to make preservation decisions. These skills are directly relevant to preserving digitized and born-digital materials, where understanding the nature and characteristics of file types is essential to assessing their authenticity, provenance, and long-term preservation needs.

Artifact 3: Python Scripts

My third piece of evidence is a pair of Python scripts I built for a personal archiving project involving renaming a large collection of home workout videos. I have learned the basics of Python but am not a proficient programmer, so I used AI assistance to help translate my ideas into working code and to troubleshoot problems as they arose. My first attempt used CLIP, a pretrained AI vision-language model, to automatically classify each video by exercise type. This approach ultimately misclassified too many videos to be useful. The results suggested a limitation of using a general-purpose vision-language model: CLIP was not sufficiently reliable at distinguishing between different human body positions. I then pivoted to a simpler, rule-based solution, developing a second script with assistance from ChatGPT. The script uses a regular expression to parse each filename for an exercise name and date, groups files by exercise, and cleans up redundant text left behind by my file transfer process. This experience demonstrates both developing my Python skills through practical use and evaluating AI technology in context. Rather than assuming that the more sophisticated AI approach would produce the best result, I tested it, identified its limitations, and ultimately chose a simpler and more reliable solution.

My fourth piece of evidence is volunteer user research work in the Library Technology Integration Lab at San Jose State, evaluating a virtual reality conference space built on the platform FrameVR. In leading a moderated user testing session, the notes for which I have included here, I observed direct, hands-on evidence of an emerging technology’s usability limitations: a participant initially didn’t realize he could speak aloud to the space’s AI-driven guide, while a “cancel” button in the interface repeatedly failed to register clicks. Also, multiple research participants said “thank you” to our space’s chatbots, which would respond that “thank you” was outside their area of knowledge! My teammate later worked on training the chatbots to handle this more gracefully. This test revealed several bugs and problems that my team subsequently worked on repairing before the space’s ultimate deployment. My experience as a user research facilitator showed me how direct observation can reveal limitations of an emerging technology that are difficult to identify through documentation or expert familiarity alone. Neither the platform’s official documentation nor our own experience with the VR environment, developed over weeks of interaction, could reveal all of the difficulties a first-time user would encounter.

My fifth piece of evidence is a set of blog discussion posts from INFO 282, responding to current articles from digital asset management (DAM) blogs about the impact of AI technology on DAM practices. In one response, discussing Model Context Protocol (MCP) architecture for AI agents managing DAM systems autonomously, I raised concerns about governance and security risk, including prompt injection, that cannot necessarily be addressed with audit trails alone. In another, discussing AI-generated content and the C2PA provenance standard, I identified a technical tension: verifying an asset’s provenance requires cryptographic signatures that break every time a file is transformed, meaning a DAM system must re-sign derivatives at each step to maintain a usable chain of custody. In a third post, responding to a published study on GPT-4’s accuracy in tagging DAM assets, I challenged the study’s optimistic framing. I argued that if properly checking AI-generated output takes as much time as producing the same output manually, the promised productivity gains may not materialize, particularly for already-overloaded workers who may be tempted to trust the technology rather than conduct thorough checks. This artifact demonstrates my ongoing critical engagement with a current technological debate, evaluating specific technical and organizational tradeoffs through engagement with industry literature.

Section 3: Conclusion

These five pieces reflect how I engage with new technology throughout my personal and professional lives: I regularly experiment with new technologies, troubleshoot them when they go wrong, and research and deploy tools based on my actual needs, not on trends or maximal features. In my future career, I intend to continue evaluating new technologies in this way, using them on work tasks before deciding whether they are worth adopting, and paying as much attention to the governance and training a technology requires as to its raw capabilities. To stay up to date in this area, I plan to follow academic journals including Journal of Library Metadata, which is directly relevant to my professional interest in innovations in digital resource management and discovery, and Journal of Digital Media Management, which covers practical DAM and rights-management technology questions similar to those I discussed above. I also plan to consult the Library of Congress’s digital preservation guidance, since format identification and metadata tools like the ones I evaluated continue to evolve alongside preservation standards.