Conclusions and Further Research

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Within this chapter, we have described many aspects associated with building privacy into agent-supported distributed learning. We describe how the LTSA model may be applied to ADL environments. Probing deeper into privacy requirements, we started with the privacy principles to interpret the technologies that may be applied to ADL to provide privacy. Trust and policy systems, policy negotiation, ways of reducing learner anxiety, trustable human–computer interfaces, secure distributed logging, anonymity systems, and network confidentiality approaches can all play a role in meeting privacy requirements in ADL implementations. We also present a privacy architecture for agent- based e-commerce that may be applied to ADL. However, it is clear that not all of these technologies would be required for every learning environment. For instance, an agent-based learning environment designed for delivering most undergraduate university or community college courses would probably not require network confidentiality or pseudonyms. On the other hand, such privacy features would be highly advantageous for e-learning environments meeting training objectives that target important new corporate strategic directions. For instance, key research staff may undertake specialized technical training closely aligned with corporate strategy. Corporations would want an e-learning service that provides a high level of confidentiality and privacy as would be provided by integrating the technologies described in this chapter. Indeed, such security technologies could be adapted for other e-business areas such as healthcare, manufacturing, business-to-business commerce, as well as general e-commerce applications.

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